Image Restoration And Reconstruction
Mean Filters
Order-Statistic Filters
Spatial Filtering: Mean Filters
Adaptive Filters
Adaptive Mean Filters
Adaptive Median Filters
Image Enhancement: Introduction to Spatial Filters, Low Pass Filter and High Pass Filters. Here Discussed Image Smoothing and Image Sharping, Gaussian Filters
its very useful for students.
Sharpening process in spatial domain
Direct Manipulation of image Pixels.
The objective of Sharpening is to highlight transitions in intensity
The image blurring is accomplished by pixel averaging in a neighborhood.
Since averaging is analogous to integration.
Prepared by
M. Sahaya Pretha
Department of Computer Science and Engineering,
MS University, Tirunelveli Dist, Tamilnadu.
This document discusses various techniques for image enhancement in the frequency domain. It describes three types of low-pass filters for smoothing images: ideal low-pass filters, Butterworth low-pass filters, and Gaussian low-pass filters. It also discusses three corresponding types of high-pass filters for sharpening images: ideal high-pass filters, Butterworth high-pass filters, and Gaussian high-pass filters. The key steps in frequency domain filtering are also summarized.
Spatial filtering is a technique that operates directly on pixels in an image. It involves sliding a filter mask over the image and applying a filtering operation using the pixels covered by the mask. Common operations include smoothing to reduce noise and sharpening to enhance edges. Smoothing filters average pixel values, while median filters select the median value. Spatial filtering can blur details and reduce noise but must address edge effects where the mask extends past image boundaries.
Digital images can be enhanced in various ways to improve quality. There are three main categories of enhancement techniques: spatial domain, frequency domain, and combination methods. Spatial domain methods operate directly on pixel values using point processing or neighborhood filtering. Key spatial techniques include contrast stretching, thresholding, and histogram equalization. Frequency domain methods modify an image's Fourier transform. Common transformations include logarithmic, power-law, and piecewise linear functions, which can increase contrast or highlight certain grayscale ranges. Proper enhancement improves an image's features for desired applications.
Digital image processing involves techniques to restore degraded images. Image restoration aims to recover the original undistorted image from a degraded observation. The degradation is typically modeled as the original image being operated on by a degradation function and additive noise. Common restoration techniques include spatial domain filters like mean, median and order-statistic filters to remove noise, and frequency domain filtering to reduce periodic noise. The choice of restoration method depends on the type and characteristics of degradation in the image.
This document discusses various frequency domain image filtering techniques. It outlines the basic steps for filtering in the frequency domain which includes centering the Fourier transform, computing the discrete Fourier transform, multiplying by a filter function, computing the inverse transform and canceling centering operations. Specific filters are then described including low pass, high pass, ideal filters and Butterworth filters. Examples of applying these filters to images are provided to demonstrate the effects. Homomorphic filtering is also introduced as a technique for illumination correction.
Morphological image processing uses mathematical morphology tools to extract image components and describe shapes. Some key tools include binary erosion and dilation, which thin and thicken objects. Erosion shrinks objects while dilation grows them. Opening and closing are combinations of erosion and dilation that smooth contours or fill gaps. The hit-or-miss transform detects shapes by requiring matches of foreground and background pixels. Other algorithms include boundary extraction, hole filling, and thinning to find skeletons, which are medial axes of object shapes.
This document provides an overview of digital image processing techniques for image restoration. It defines image restoration as improving a degraded image using prior knowledge of the degradation process. The goal is to recover the original image by applying an inverse process to the degradation function. Common degradation sources are discussed, along with noise models like Gaussian, salt and pepper, and periodic noise. Spatial and frequency domain filtering techniques are presented for restoration, such as mean, median and inverse filters. The maximum mean square error or Wiener filter is also introduced as a way to minimize restoration error.
This document discusses image segmentation techniques. It describes how segmentation partitions an image into meaningful regions based on discontinuities or similarities in pixel intensity. The key methods covered are thresholding, edge detection using gradient and Laplacian operators, and the Hough transform for global line detection. Adaptive thresholding is also introduced as a technique to handle uneven illumination.
Spatial filtering using image processingAnuj Arora
油
(1) Spatial filtering is defined as operations performed on pixels within a neighborhood of an image using a mask or kernel. (2) Filters can be used to blur/smooth an image by reducing noise or sharpen an image by enhancing edges. (3) Common linear filtering methods include averaging, Gaussian, and derivative filters which are implemented using various mask patterns to modify pixels in the filtered image.
Image filtering in Digital image processingAbinaya B
油
This document discusses various image filtering techniques used for modifying or enhancing digital images. It describes spatial domain filters such as smoothing filters including averaging and weighted averaging filters, as well as order statistics filters like median filters. It also covers frequency domain filters including ideal low pass, Butterworth low pass, and Gaussian low pass filters for smoothing, as well as their corresponding high pass filters for sharpening. Examples of applying different filters at different cutoff frequencies are provided to illustrate their effects.
COM2304: Intensity Transformation and Spatial Filtering I (Intensity Transf...Hemantha Kulathilake
油
At the end of this lesson, you should be able to;
describe spatial domain of the digital image.
recognize the image enhancement techniques.
describe and apply the concept of intensity transformation.
express histograms and histogram processing.
describe image noise.
characterize the types of Noise.
describe concept of image restoration.
Sharpening using frequency Domain Filterarulraj121
油
This document discusses frequency domain filtering for image sharpening. It begins by explaining the difference between spatial and frequency domain image enhancement techniques. It then describes the basic steps for filtering in the frequency domain, which involves taking the Fourier transform of an image, multiplying it by a filter function, and taking the inverse Fourier transform. The document discusses sharpening filters specifically, noting that high-pass filters can be used to sharpen by preserving high frequency components that represent edges. It provides examples of ideal low-pass and high-pass filters, and Butterworth and Gaussian filters. Laplacian filters are also introduced as a common sharpening filter that uses an approximation of second derivatives to detect and enhance edges.
This document discusses image enhancement techniques in the spatial domain. It begins by introducing intensity transformations and spatial filtering as the two principal categories of spatial domain processing. It then describes the basics of intensity transformations, including how they directly manipulate pixel values in an image. The document focuses on different types of basic intensity transformation functions such as image negation, log transformations, power law transformations, and piecewise linear transformations. It provides examples of how these transformations can be used to enhance images. Finally, it discusses histogram processing and how the histogram of an image provides information about the distribution of pixel intensities.
This document discusses various intensity transformation and spatial filtering techniques for digital image enhancement. It covers single pixel operations like negative image and contrast stretching. It also discusses neighborhood operations such as averaging and median filters. Finally, it discusses geometric spatial transformations like scaling, rotation and translation. The document provides details on basic intensity transformation functions including log, power law, and piecewise linear transformations. It also covers histogram processing techniques like histogram equalization, matching and local histogram processing. Spatial filtering and its mechanics are explained.
The document discusses image restoration techniques. It introduces common image degradation models and noise models encountered in imaging. Spatial and frequency domain filtering methods are described for restoration when the degradation is additive noise. Adaptive median filtering and frequency domain filtering techniques like bandreject, bandpass and notch filters are explained for periodic noise removal. Optimal filtering methods like Wiener filtering that minimize mean square error are also covered. The document provides an overview of key concepts and methods in image restoration.
The document discusses image restoration and reconstruction techniques. It covers topics like image restoration models, noise models, spatial filtering, inverse filtering, Wiener filtering, Fourier slice theorem, computed tomography principles, Radon transform, and filtered backprojection reconstruction. As an example, it derives the analytical expression for the projection of a circular object using the Radon transform, showing that the projection is independent of angle and equals 2Ar(r2-2) when r.
This document summarizes techniques for least mean square filtering and geometric transformations. It discusses minimum mean square error (Wiener) filtering, constrained least squares filtering, and geometric mean filtering for noise removal. It also covers spatial transformations, nearest neighbor gray level interpolation, and bilinear interpolation for geometric correction of distorted images. Examples are provided to demonstrate geometric distortion, nearest neighbor interpolation, and bilinear transformation.
This presentation describes briefly about the image enhancement in spatial domain, basic gray level transformation, histogram processing, enhancement using arithmetic/ logical operation, basics of spatial filtering and local enhancements.
Thresholding is a technique for image segmentation where each pixel is classified as either foreground or background based on a threshold value. It can be used for images with light objects and a dark background by selecting a threshold that separates the intensities. More generally, multilevel thresholding can classify pixels into object classes or background based on multiple threshold values. Thresholding views segmentation as a test against a threshold function of pixel location and intensity. Global thresholding uses a single threshold across the image while adaptive thresholding uses local thresholds.
This document discusses image restoration and reconstruction techniques for noise removal. It begins by defining image restoration as attempting to reverse degradation processes to restore degraded images. Various noise models are described, including Gaussian, Rayleigh, Erlang, exponential, uniform, and impulse noise. Spatial domain filtering techniques like mean, median, and order statistics filters are covered for noise removal. Frequency domain filtering using band reject filters is also discussed, as well as adaptive filtering techniques. Examples are provided to demonstrate noise removal.
The document discusses image sampling and quantization. It defines a digital image as a discrete 2D array containing intensity values of finite bits. A digital image is formed by sampling a continuous image, which involves multiplying it by a comb function of discrete delta pulses, yielding discrete image values. Quantization further discretizes the intensity values into a finite set of values. For accurate image reconstruction, the sampling frequency must be greater than twice the maximum image frequency, as stated by the sampling theorem.
This document discusses color image processing and provides information on various color models and color fundamentals. It describes full-color and pseudo-color processing, color fundamentals including the visible light spectrum, color perception by the human eye, and color properties. It also summarizes RGB, CMY/CMYK, and HSI color models, conversions between models, and methods for pseudo-color image processing including intensity slicing and intensity to color transformations.
The document discusses noise models and methods for removing additive noise from digital images. It describes several types of noise that can affect images, such as Gaussian, impulse, uniform, Rayleigh, gamma and exponential noise. It also presents various noise filters that can be used to remove noise, including mean filters like arithmetic, geometric and harmonic filters, and order statistics filters such as median, max, min and midpoint filters. The filters aim to reduce noise while retaining image detail as much as possible.
Spatial filtering involves applying filters or kernels to images to enhance or modify pixel values based on neighboring pixel values. Linear spatial filtering involves taking a weighted sum of pixel values within the filter window. Common filters include averaging filters for noise reduction, median filters to reduce impulse noise while preserving edges, and sharpening filters like Laplacian filters and unsharp masking to enhance details.
The document discusses image restoration techniques to recover degraded images. It describes modeling image degradation using a degradation function and additive noise. Common noise sources and models are explained, including Gaussian, Rayleigh, Erlang, exponential, uniform, and impulse noise. Spatial filtering techniques for noise removal are covered, such as mean, order-statistic (median, max, min), and adaptive filters. Adaptive median filters are discussed that vary the filter window size until the median pixel value is not an impulse value. The goal of image restoration is to apply the inverse of the degradation process to recover the original undamaged image.
Morphological image processing uses mathematical morphology tools to extract image components and describe shapes. Some key tools include binary erosion and dilation, which thin and thicken objects. Erosion shrinks objects while dilation grows them. Opening and closing are combinations of erosion and dilation that smooth contours or fill gaps. The hit-or-miss transform detects shapes by requiring matches of foreground and background pixels. Other algorithms include boundary extraction, hole filling, and thinning to find skeletons, which are medial axes of object shapes.
This document provides an overview of digital image processing techniques for image restoration. It defines image restoration as improving a degraded image using prior knowledge of the degradation process. The goal is to recover the original image by applying an inverse process to the degradation function. Common degradation sources are discussed, along with noise models like Gaussian, salt and pepper, and periodic noise. Spatial and frequency domain filtering techniques are presented for restoration, such as mean, median and inverse filters. The maximum mean square error or Wiener filter is also introduced as a way to minimize restoration error.
This document discusses image segmentation techniques. It describes how segmentation partitions an image into meaningful regions based on discontinuities or similarities in pixel intensity. The key methods covered are thresholding, edge detection using gradient and Laplacian operators, and the Hough transform for global line detection. Adaptive thresholding is also introduced as a technique to handle uneven illumination.
Spatial filtering using image processingAnuj Arora
油
(1) Spatial filtering is defined as operations performed on pixels within a neighborhood of an image using a mask or kernel. (2) Filters can be used to blur/smooth an image by reducing noise or sharpen an image by enhancing edges. (3) Common linear filtering methods include averaging, Gaussian, and derivative filters which are implemented using various mask patterns to modify pixels in the filtered image.
Image filtering in Digital image processingAbinaya B
油
This document discusses various image filtering techniques used for modifying or enhancing digital images. It describes spatial domain filters such as smoothing filters including averaging and weighted averaging filters, as well as order statistics filters like median filters. It also covers frequency domain filters including ideal low pass, Butterworth low pass, and Gaussian low pass filters for smoothing, as well as their corresponding high pass filters for sharpening. Examples of applying different filters at different cutoff frequencies are provided to illustrate their effects.
COM2304: Intensity Transformation and Spatial Filtering I (Intensity Transf...Hemantha Kulathilake
油
At the end of this lesson, you should be able to;
describe spatial domain of the digital image.
recognize the image enhancement techniques.
describe and apply the concept of intensity transformation.
express histograms and histogram processing.
describe image noise.
characterize the types of Noise.
describe concept of image restoration.
Sharpening using frequency Domain Filterarulraj121
油
This document discusses frequency domain filtering for image sharpening. It begins by explaining the difference between spatial and frequency domain image enhancement techniques. It then describes the basic steps for filtering in the frequency domain, which involves taking the Fourier transform of an image, multiplying it by a filter function, and taking the inverse Fourier transform. The document discusses sharpening filters specifically, noting that high-pass filters can be used to sharpen by preserving high frequency components that represent edges. It provides examples of ideal low-pass and high-pass filters, and Butterworth and Gaussian filters. Laplacian filters are also introduced as a common sharpening filter that uses an approximation of second derivatives to detect and enhance edges.
This document discusses image enhancement techniques in the spatial domain. It begins by introducing intensity transformations and spatial filtering as the two principal categories of spatial domain processing. It then describes the basics of intensity transformations, including how they directly manipulate pixel values in an image. The document focuses on different types of basic intensity transformation functions such as image negation, log transformations, power law transformations, and piecewise linear transformations. It provides examples of how these transformations can be used to enhance images. Finally, it discusses histogram processing and how the histogram of an image provides information about the distribution of pixel intensities.
This document discusses various intensity transformation and spatial filtering techniques for digital image enhancement. It covers single pixel operations like negative image and contrast stretching. It also discusses neighborhood operations such as averaging and median filters. Finally, it discusses geometric spatial transformations like scaling, rotation and translation. The document provides details on basic intensity transformation functions including log, power law, and piecewise linear transformations. It also covers histogram processing techniques like histogram equalization, matching and local histogram processing. Spatial filtering and its mechanics are explained.
The document discusses image restoration techniques. It introduces common image degradation models and noise models encountered in imaging. Spatial and frequency domain filtering methods are described for restoration when the degradation is additive noise. Adaptive median filtering and frequency domain filtering techniques like bandreject, bandpass and notch filters are explained for periodic noise removal. Optimal filtering methods like Wiener filtering that minimize mean square error are also covered. The document provides an overview of key concepts and methods in image restoration.
The document discusses image restoration and reconstruction techniques. It covers topics like image restoration models, noise models, spatial filtering, inverse filtering, Wiener filtering, Fourier slice theorem, computed tomography principles, Radon transform, and filtered backprojection reconstruction. As an example, it derives the analytical expression for the projection of a circular object using the Radon transform, showing that the projection is independent of angle and equals 2Ar(r2-2) when r.
This document summarizes techniques for least mean square filtering and geometric transformations. It discusses minimum mean square error (Wiener) filtering, constrained least squares filtering, and geometric mean filtering for noise removal. It also covers spatial transformations, nearest neighbor gray level interpolation, and bilinear interpolation for geometric correction of distorted images. Examples are provided to demonstrate geometric distortion, nearest neighbor interpolation, and bilinear transformation.
This presentation describes briefly about the image enhancement in spatial domain, basic gray level transformation, histogram processing, enhancement using arithmetic/ logical operation, basics of spatial filtering and local enhancements.
Thresholding is a technique for image segmentation where each pixel is classified as either foreground or background based on a threshold value. It can be used for images with light objects and a dark background by selecting a threshold that separates the intensities. More generally, multilevel thresholding can classify pixels into object classes or background based on multiple threshold values. Thresholding views segmentation as a test against a threshold function of pixel location and intensity. Global thresholding uses a single threshold across the image while adaptive thresholding uses local thresholds.
This document discusses image restoration and reconstruction techniques for noise removal. It begins by defining image restoration as attempting to reverse degradation processes to restore degraded images. Various noise models are described, including Gaussian, Rayleigh, Erlang, exponential, uniform, and impulse noise. Spatial domain filtering techniques like mean, median, and order statistics filters are covered for noise removal. Frequency domain filtering using band reject filters is also discussed, as well as adaptive filtering techniques. Examples are provided to demonstrate noise removal.
The document discusses image sampling and quantization. It defines a digital image as a discrete 2D array containing intensity values of finite bits. A digital image is formed by sampling a continuous image, which involves multiplying it by a comb function of discrete delta pulses, yielding discrete image values. Quantization further discretizes the intensity values into a finite set of values. For accurate image reconstruction, the sampling frequency must be greater than twice the maximum image frequency, as stated by the sampling theorem.
This document discusses color image processing and provides information on various color models and color fundamentals. It describes full-color and pseudo-color processing, color fundamentals including the visible light spectrum, color perception by the human eye, and color properties. It also summarizes RGB, CMY/CMYK, and HSI color models, conversions between models, and methods for pseudo-color image processing including intensity slicing and intensity to color transformations.
The document discusses noise models and methods for removing additive noise from digital images. It describes several types of noise that can affect images, such as Gaussian, impulse, uniform, Rayleigh, gamma and exponential noise. It also presents various noise filters that can be used to remove noise, including mean filters like arithmetic, geometric and harmonic filters, and order statistics filters such as median, max, min and midpoint filters. The filters aim to reduce noise while retaining image detail as much as possible.
Spatial filtering involves applying filters or kernels to images to enhance or modify pixel values based on neighboring pixel values. Linear spatial filtering involves taking a weighted sum of pixel values within the filter window. Common filters include averaging filters for noise reduction, median filters to reduce impulse noise while preserving edges, and sharpening filters like Laplacian filters and unsharp masking to enhance details.
The document discusses image restoration techniques to recover degraded images. It describes modeling image degradation using a degradation function and additive noise. Common noise sources and models are explained, including Gaussian, Rayleigh, Erlang, exponential, uniform, and impulse noise. Spatial filtering techniques for noise removal are covered, such as mean, order-statistic (median, max, min), and adaptive filters. Adaptive median filters are discussed that vary the filter window size until the median pixel value is not an impulse value. The goal of image restoration is to apply the inverse of the degradation process to recover the original undamaged image.
This document discusses various image restoration techniques in the presence of noise. It begins by explaining that image denoising aims to remove noise while retaining important signal features, which can be done through linear or non-linear filtering. It then describes several types of spatial filters that are commonly used for image smoothing, sharpening, and noise removal, including mean filters, order statistic filters, and median filters. It provides details on how various mean filters like arithmetic, geometric, and harmonic mean filters operate and their effectiveness on different noise types. Order statistic filters and median filters are highlighted as being well-suited for salt-and-pepper noise removal. The document also includes examples and equations to illustrate key image restoration concepts.
Accelerated Joint Image Despeckling Algorithm in the Wavelet and Spatial DomainsCSCJournals
油
This document summarizes an algorithm for reducing speckle noise in images using a two-stage approach combining wavelet and spatial domain filtering. The first stage estimates the optimal parameter value for a spatial speckle reduction filter based on edge pixel statistics and noise variance. The second stage then uses the optimized spatial filter to additionally smooth wavelet approximation sub-band coefficients. A complexity reduction method for wavelet decomposition is also proposed. Existing noise reduction methods like the Lee, Kuan and Frost filters are reviewed for context. The results of applying the proposed two-stage algorithm are promising in terms of improved image quality.
Intensity transformations and spatial filtering techniques can enhance images by modifying pixel intensities or applying neighborhood operations. Key techniques include:
1. Grayscale transformations like logarithms, power laws, and piecewise linear functions which compress or expand tonal ranges. Histogram processing includes equalization to spread intensities uniformly.
2. Spatial filters apply operations to pixels based on neighboring values. Smoothing filters reduce noise while sharpening filters using derivatives enhance edges. The Laplacian is a second derivative filter useful for edge detection.
3. Unsharp masking and high-boost filtering enhance edges by subtracting a blurred version of an image from the original, emphasizing differences.
Histogram Processing
Histogram Equalization
Histogram Matching
Local Histogram processing
Using histogram statistics for image enhancement
Uses for Histogram Processing
Histogram Equalization
Histogram Matching
Local Histogram Processing
Basics of Spatial Filtering
This document discusses various techniques for image restoration. It begins by explaining the degradation model and how restoration filters can be used to estimate the original image. It then describes several order statistics filters for noise removal, including median filters which are effective for salt and pepper noise, and maximum/minimum filters for pepper/salt noise respectively. The document also discusses midpoint filters, alpha-trimmed mean filters, and how band reject filters can be used to remove periodic noise by filtering specific frequency ranges from the image. In conclusion, it emphasizes that the goal of restoration is to estimate the degradation function and recover the original image by reversing the degradation process either in the spatial or frequency domain.
Basic Introduction about Image Restoration (Order Statistics Filters)
Median Filter
Max and Min Filter
MidPoint Filter
Alpha-trimmed Mean filter.
and Brief Introduction to Periodic Noise
Any Question contact kalyan.acharjya@gmail.com
Noise models presented by Nisha Menon KNisha Menon K
油
The document discusses noise models and restoration of noisy images. It describes various types of noise that can affect digital images, such as Gaussian noise, salt and pepper noise, and periodic noise. Spatial filtering techniques for image restoration are presented, including mean filters, median filters, and adaptive filters. Mean filters are suited for Gaussian noise while median filters are effective for salt and pepper noise. Adaptive filters change behavior based on local image characteristics to better preserve edges during noise removal. The document provides examples of filtering noisy images corrupted with different types of noise.
This document discusses various spatial filtering and image enhancement techniques including intensity transformation, smoothing filters, sharpening filters, and combining multiple techniques. It covers linear and non-linear spatial filters, smoothing filters like averaging and median filters for noise reduction, and sharpening filters such as unsharp masking, high-boost filtering, and gradient-based methods. Examples are provided to demonstrate the use of various filters for tasks like noise removal, edge enhancement, and combining techniques for improved image quality.
The document discusses various image enhancement techniques in the spatial domain. It covers basic gray level transformations like negatives, log transformations, and power law transformations. It also discusses histogram processing and enhancement using arithmetic operations. Furthermore, it explains smoothing and sharpening spatial filters, and how to combine different spatial enhancement methods. The document provides examples and background on these fundamental image enhancement concepts.
D ESIGN A ND I MPLEMENTATION OF D IGITAL F ILTER B ANK T O R EDUCE N O...sipij
油
The main theme of this paper is to reduce noise fro
m the noisy composite signal and reconstruct the in
put
signals from the composite signal by designing FIR
digital filter bank. In this work, three sinusoidal
signals
of different frequencies and amplitudes are combine
d to get composite signal and a low frequency noise
signal is added with the composite signal to get no
isy composite signal. Finally noisy composite signa
l is
filtered by using FIR digital filter bank to reduce
noise and reconstruct the input signals
The document discusses different types of mean filters and order statistics filters used for noise reduction in images. It describes four types of mean filters: arithmetic, geometric, harmonic, and contraharmonic. The arithmetic mean filter computes the average value within a window. The geometric mean filter takes the product of pixels within a window. The harmonic mean filter calculates the inverse average of inverse pixel values. The contraharmonic mean filter uses a parameter to reduce salt and pepper noise. Order statistics filters like the median filter replace pixel values with the median within a window, effectively reducing noise.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document discusses image restoration and segmentation. Image restoration deals with improving degraded images by removing noise and blurring. Various noise models and filters for restoration are described, including mean filters, order statistics filters, adaptive filters, and frequency domain filters. Segmentation involves separating an image into regions or objects. Methods described include edge detection, region-based segmentation, and morphological operations like erosion and dilation.
Defining the Future of Biophilic Design in Crete.pdfARENCOS
油
Biophilic design is emerging as a key approach to enhancing well-being by integrating natural elements into residential architecture. In Crete, where the landscape is rich with breathtaking sea views, lush olive groves, and dramatic mountains, biophilic design principles can be seamlessly incorporated to create healthier, more harmonious living environments.
Biases, our brain and software developmentMatias Iacono
油
Quick presentation about cognitive biases, classic psychological researches and quite new papers that displays how those biases might be impacting software developers.
The Golden Gate Bridge a structural marvel inspired by mother nature.pptxAkankshaRawat75
油
The Golden Gate Bridge is a 6 lane suspension bridge spans the Golden Gate Strait, connecting the city of San Francisco to Marin County, California.
It provides a vital transportation link between the Pacific Ocean and the San Francisco Bay.
About
Practice Head is assembled with Practice Torpedo intended for carrying out exercise firings. It is assembled with Homing Head in the forward section and oxygen flask in the rear section. Practice Head imparts positive buoyancy to the Torpedo at the end of run. The Practice Head is divided into two compartments viz. Ballast Compartment (Houses Light Device, Depth & Roll Recorder, Signal Flare Ejector, Discharge Valve, Stop Cock, Water discharge Valve, Bellow reducing Valve, Release Mechanism, Recess, Bypass Valve, Pressure Equalizer, Float, Sinking Plug etc.) which provides positive buoyancy at the end of run by discharging water (140 ltrs.) filled in the compartment and Instrument compartment (dry), houses (safety & recovery unit and its battery, combined homing and influence exploder equipment, noise maker, bollards & safety valve etc.) The recess in Ballast compartment houses the float which gets inflated at the end of run to provide floatation to the surfaced Torpedo. Several hand holes/recesses are provided on the casing/shell of Practice Head for assembly of the following components:-
a) Signal Flare Ejector Assembly
b) Depth and Roll Recorder Assembly
c) Light Device
d) Pressure equalizer
e) Drain/Discharge Valve assembly
f) Bollard Assembly
g) Holding for Floater/Balloon Assembly
h) Sinking Valve
i) Safety Valve
j) Inspection hand hole
Technical Details:
SrNo Items Specifications
1 Aluminum Alloy (AlMg5)
Casing Body Material: AlMg5
Larger Outer Diameter of the Casing: 532.4 MM
Smaller Outer Diameter of the Casing: 503.05 MM
Total Length: 1204.20 MM
Thickness: 6-8 mm
Structural Details of Casing: The casing is of uniform outer dia for a certain distance from rear side and tapered from a definite distance to the front side. (Refer T-DAP-A1828-GADWG-PH- REV 00)
Slope of the Tapered Portion: 1/8
Mass of Casing (Without components mounting, but including the ribs and collars on the body): 58.5 kg
Maximum External Test Pressure: 12 kgf/cm2
Maximum Internal Test Pressure:-
i. For Ballast Compartment: 2 kgf/cm2
ii. For Instrument Compartment: 1 kgf/cm2
Innerspace of casing assembly have 2 compartments:-
i. Ballast Compartment and
ii. Instrument Compartment
Cut outs/ recesses shall be provided for the assembly of following components.
a) Signal Flare Ejector Assembly
b) Depth and Roll Recorder Assembly
c) Light Device
d) Pressure Equalizer
e) Drain/ discharge valve assembly
2 Front Side Collar Material: AlMg5
Maximum Outer Diameter: 500 MM
Pitch Circle Diameter: 468 MM
All Dimensions as per drawing T-DAP-A1828-MDWG-C&R-REV-00
Application:
In a torpedo, the ballast components and instrument compartment play crucial roles in maintaining stability, control, and overall operational effectiveness. The ballast system primarily manages buoyancy and trim, ensuring that the torpedo maintains a stable trajectory underwater.
The Uni-Bell PVC Pipe Association (PVCPA) has published the first North American industry-wide environmental product declaration (EPD) for water and sewer piping, and it has been verified by NSF Sustainability, a division of global public health organization NSF International.
Flex and rigid-flex printed circuit boards (PCBs) can be considered at the basic level some of the most complex PCBs in the industry. With that in mind, its incredibly easy to make a mistake, to leave something out, or to create a design that was doomed from the start.
Such design failures can end up leading to an eventual failure by delamination, short circuits, damage to the flex portions, and many other things. The easiest way to circumvent these is to start at the beginning, to design with preventing failure in mind rather than trying to fix existing designs to accommodate for problems.
In this webinar, we cover how to design flex and rigid-flex PCBs with failure prevention in mind to save time, money, and headaches, and what failure can look like.
For more information on our flex and rigid-flex PCB solutions, visit https://www.epectec.com/flex.
How to Build a Speed Sensor using Arduino?CircuitDigest
油
Learn how to measure speed using IR sensors in this simple DIY project. This tutorial cover circuit diagram, Sensor calibration and speed calculations and optimized Arduino code for real time speed measurements.
INVESTIGATION OF PUEA IN COGNITIVE RADIO NETWORKS USING ENERGY DETECTION IN D...csijjournal
油
Primary User Emulation Attack (PUEA) is one of the major threats to the spectrum sensing in cognitive
radio networks. This paper studies the PUEA using energy detection that is based on the energy of the
received signal. It discusses the impact of increasing the number of attackers on the performance of
secondary user. Moreover, studying how the malicious user can emulate the Primary User (PU) signal is
made. This is the first analytical method to study PUEA under a different number of attackers. The
detection of the PUEA increases with increasing the number of attackers and decreases when changing the
channel from lognormal to Rayleigh fading.
3. Outline
Restoration in the Presence of Noise Only
Spatial Filtering
Mean Filters
Order-Statistic Filters
Model of image degradation/restoration
process
3
4. 2/16/2018 4
Restoration in the Presence of Noise Only
牟 Spatial Filtering
Noise model without degradation
( , ) ( , ) ( , )
and
( , ) ( , ) ( , )
g x y f x y x y
G u v F u v N u v
5. 2/16/2018 5
Spatial Filtering: Mean Filters (1)
Let represent the set of coordinates in a rectangle
subimage window of size , centered at ( , ).
xyS
m n x y
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( , )
Arithmetic mean filter
1
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xys t S
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6. 2/16/2018 6
Spatial Filtering: Mean Filters (2)
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1
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Geometric mean filter
( , ) ( , )
xy
mn
s t S
f x y g s t
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Generally, a geometric mean filter achieves smoothing
comparable to the arithmetic mean filter, but it tends to lose
less image detail in the process
7. 2/16/2018 7
Spatial Filtering: Mean Filters (3)
袖
( , )
Harmonic mean filter
( , )
1
( , )xys t S
mn
f x y
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It works well for salt noise, but fails for pepper noise.
It does well also with other types of noise like Gaussian noise.
8. 2/16/2018 8
Spatial Filtering: Mean Filters (4)
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1
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( , )
Contraharmonic mean filter
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( , )
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Q
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g s t
Q is the order of the filter.
It is well suited for reducing the effects of salt-and-pepper
noise. Q>0 for pepper noise and Q<0 for salt noise.
12. 2/16/2018 12
Spatial Filtering: Order-Statistic Filters (1)
袖 ( , )
Max filter
( , ) max ( , )
xys t S
f x y g s t
袖 ( , )
Median filter
( , ) ( , )
xys t S
f x y median g s t
袖 ( , )
Min filter
( , ) min ( , )
xys t S
f x y g s t
13. 2/16/2018 13
Spatial Filtering: Order-Statistic Filters (2)
袖 ( , )( , )
Midpoint filter
1
( , ) max ( , ) min ( , )
2 xyxy s t Ss t S
f x y g s t g s t
刻 削
14. Spatial Filtering: Order-Statistic Filters (3)
袖
( , )
Alpha-trimmed mean filter
1
( , ) ( , )
xy
r
s t S
f x y g s t
mn d
2/16/2018 14
We delete the / 2 lowest and the / 2 highest intensity values of
( , ) in the neighborhood . Let ( , ) represent the remaining
- pixels.
xy r
d d
g s t S g s t
mn d
18. 2/16/2018 18
Spatial Filtering: Adaptive Filters (1)
Adaptive filters
The behavior changes based on statistical characteristics of
the image inside the filter region defined by the mn
rectangular window.
The performance is superior to that of the filters discussed
19. 2/16/2018 19
Adaptive Filters:
Adaptive, Local Noise Reduction Filters (1)
2
: local region
The response of the filter at the center point (x,y) of
is based on four quantities:
(a) ( , ), the value of the noisy image at ( , );
(b) , the variance of the noise corrupti
xy
xy
S
S
g x y x y
2
ng ( , )
to form ( , );
(c) , the local mean of the pixels in ;
(d) , the local variance of the pixels in .
L xy
L xy
f x y
g x y
m S
S
20. 2/16/2018 20
Adaptive Filters:
Adaptive, Local Noise Reduction Filters (2)
2
2
The behavior of the filter:
(a) if is zero, the filter should return simply the value
of ( , ).
(b) if the local variance is high relative to ,the filter
should return a value cl
g x y
ose to ( , );
(c) if the two variances are equal, the filter returns the
arithmetic mean value of the pixels in .xy
g x y
S
21. 2/16/2018 21
Adaptive Filters:
Adaptive, Local Noise Reduction Filters (3)
袖
袖
2
2
An adaptive expression for obtaining ( , )
based on the assumptions:
( , ) ( , ) ( , ) L
L
f x y
f x y g x y g x y m
23. 2/16/2018 23
Adaptive Filters:
Adaptive Median Filters (1)
min
max
med
max
The notation:
minimum intensity value in
maximum intensity value in
median intensity value in
intensity value at coordinates ( , )
maximum all
xy
xy
xy
xy
z S
z S
z S
z x y
S
owed size of xyS
24. 2/16/2018 24
Adaptive Filters:
Adaptive Median Filters (2)
med min med max
max
The adaptive median-filtering works in two stages:
Stage A:
A1 = ; A2 =
if A1>0 and A2<0, go to stage B
Else increase the window size
if window size , re
z z z z
S
med
min max
med
peat stage A; Else output
Stage B:
B1 = ; B2 =
if B1>0 and B2<0, output ; Else output
xy xy
xy
z
z z z z
z z
25. 2/16/2018 25
Adaptive Filters:
Adaptive Median Filters (2)
med min med max
max
The adaptive median-filtering works in two stages:
Stage A:
A1 = ; A2 =
if A1>0 and A2<0, go to stage B
Else increase the window size
if window size , re
z z z z
S
med
min max
med
peat stage A; Else output
Stage B:
B1 = ; B2 =
if B1>0 and B2<0, output ; Else output
xy xy
xy
z
z z z z
z z
The median filter output
is an impulse or not
The processed point is an
impulse or not