The document proposes a single image super-resolution method that combines multi-image and example-based super-resolution by leveraging patch redundancy. It models the super-resolution problem using similar patches within an image (multi-image approach) and across image scales (example-based approach). Experimental results show the proposed method performs better than interpolation and example-based approaches at enhancing detail in low resolution images.
The document proposes a new framework called structure-modulated sparse representation (SMSR) for single image super-resolution. Existing super-resolution methods increase artifacts and do not consider image structure. The proposed SMSR algorithm formulates an optimization problem using gradient priors and nonlocal sparsity to reconstruct high-resolution images. It exploits multi-scale similarity using multi-step magnification and ridge regression for initial estimation. The algorithm also incorporates gradient histogram preservation as a regularization term. Experimental results show the proposed method outperforms state-of-the-art methods in recovering fine structures and details from low-resolution images.
The document discusses superresolution technology that can improve the resolution of infrared camera images. It begins by explaining the basic problem that small objects may be invisible or measured incorrectly in infrared images due to pixel size limitations. It then describes how superresolution works by using multiple images and deconvolution algorithms to effectively decrease pixel pitch by 1.6x and increase usable resolution also by 1.6x compared to normal images. Experimental results show that superresolution detects spatial frequencies about 50% higher than the camera's detector cutoff and improves temperature measurement accuracy compared to interpolation. The technology will be available as a software update for all current Testo infrared cameras.
1. The document presents a method for super resolution of text images using ant colony optimization. It involves registering multiple low resolution images, fusing them, performing soft classification to assign pixel values to multiple classes, and using ant colony optimization for super resolution mapping to increase the resolution.
2. Key steps include SURF-based image registration, intensity-based and discrete wavelet transform fusion, decision tree-based soft classification, and ant colony optimization to assign pixel values based on pheromone updating to increase resolution.
3. Test cases on images with angular displacement, blurred text, etc. show that the method increases resolution successfully but can add some noise, though processing is faster than alternatives. Ant colony optimization
Super Resolution in Digital Image processingRamrao Desai
油
Super-resolution aims to enhance image resolution by exploiting multiple low-resolution images. Key techniques include Bayesian methods using priors, Wiener filtering, Markov random fields, and learned models from example images. Super-resolution involves modeling blurring, sampling, and aliasing effects, and using techniques like deconvolution and example-based learning to recover high-frequency details beyond the Nyquist limit. It requires accurate motion estimation and modeling of the imaging process to combine information from multiple low-resolution images.
This document discusses decimation and interpolation using polyphase filters. It begins by defining decimation and interpolation and describing how decimators use anti-aliasing filters followed by downsamplers, while interpolators use upsamplers followed by anti-imaging filters. It then presents the principles of polyphase decimation and interpolation, showing how they can be represented in both the time and z-transform domains. This allows implementing decimators and interpolators more efficiently using fewer filter operations and less memory than traditional implementations.
This document discusses inverse distance weighting (IDW) interpolation, which is a technique used to estimate unknown values between known data points. IDW assumes that points closer to one another are more alike than distant points. The interpolated value is a weighted average of known points, with the weights being inversely proportional to the distances. This allows for the creation of continuous surfaces like elevation or temperature from point data. A case study examines the relationship between weekly rainfall patterns and dengue outbreaks in Sri Lanka using IDW and GIS tools to model spatial and temporal associations and identify potential risk areas.
Deep learning for image super resolutionPrudhvi Raj
油
Using Deep Convolutional Networks, the machine can learn end-to-end mapping between the low/high-resolution images. Unlike traditional methods, this method jointly optimizes all the layers of the image. A light-weight CNN structure is used, which is simple to implement and provides formidable trade-off from the existential methods.
6. Only for
Maxus
弌仂亟亠亢舒仆亳亠
于亠亟亠仆亳亠
Contourlet learning
Repetitive structures
Contour stencils
Softcuts
6
CS MSU Graphics & Media Lab (Video Group)
7. Only for
Contourlet learning
Maxus
仂仆仍亠-仗亠仂弍舒亰仂于舒仆亳亠
仂仂亢亠 仆舒 于亠亶于仍亠-仗亠仂弍舒亰仂于舒仆亳亠
弌仂舒仆磳 于仂从亳亠 舒仂 仗仂
亟亳舒亞仂仆舒仍仆仄 仆舒仗舒于仍亠仆亳礆
舒舒仄亠 舒亰仍仂亢亠仆亳:
仂仍亳亠于仂 仂于仆亠亶 舒亰仍仂亢亠仆亳
仂仍亳亠于仂 仆舒仗舒于仍亠仆亳亶 舒亰仍仂亢亠仆亳 仆舒
从舒亢亟仂仄 仂于仆亠
Jiji, C. V., and S. Chaudhuri, Single-frame image super-
resolution through ontourlet learning., EURASIP, 2006 7
CS MSU Graphics & Media Lab (Video Group)
8. Only for
Contourlet learning
Maxus
仂仆仍亠-仗亠仂弍舒亰仂于舒仆亳亠
仂仍亳亠于仂 仆舒仗舒于仍亠仆亳亶 舒亰仍仂亢亠仆亳 仆舒
从舒亢亟仂仄 仂于仆亠 仄仂亢亠 仂仍亳舒
Jiji, C. V., and S. Chaudhuri, Single-frame image super-
resolution through ontourlet learning., EURASIP, 2006 8
CS MSU Graphics & Media Lab (Video Group)
9. Only for
Contourlet learning
Maxus
亳仄亠 舒亰仍仂亢亠仆亳
3 仂于仆
8 仆舒仗舒于仍亠仆亳亶 舒亰仍仂亢亠仆亳
仆舒 仗仂仍亠亟仆亠仄 仂于仆亠
仂仍亳亠于仂 仆舒仗舒于仍亠仆亳亶
仆舒 从舒亢亟仂仄 仂于仆亠 仄仂亢亠 弍
舒亰仆仄
Jiji, C. V., and S. Chaudhuri, Single-frame image super-
resolution through ontourlet learning., EURASIP, 2006 9
CS MSU Graphics & Media Lab (Video Group)
10. Only for
Contourlet learning
Maxus
仗从亠亶仍 从舒亳仆仂从
丕于亠仍亳亠仆亳亠 舒亰亠亠仆亳 仂
于仂舒仆仂于仍亠仆亳亠 亠 仂亟仆仂亞仂 仂于仆
舒亰仍仂亢亠仆亳.
舒 仆亠从仂仂仂仄 仆舒弍仂亠 亳亰仂弍舒亢亠仆亳亶
仂亰亟舒仄 弍舒亰 仂仂于亠于亳亶
<亠亟仆亳亠 舒仂 于仂从亳亠 舒仂>
仗仂仍亰亠仄 弍舒亰 于 舒弍仂亠
舒仍亞仂亳仄舒: 仗仂 亠亟仆亠舒仂仆仂亶
仂舒于仍ム亠亶 仗仂亟弍亳舒亠仄 仆舒亳弍仂仍亠亠
于亠仂仆 于仂从仂舒仂仆
Jiji, C. V., and S. Chaudhuri, Single-frame image super-
resolution through ontourlet learning., EURASIP, 2006 10
CS MSU Graphics & Media Lab (Video Group)
11. Only for
Contourlet learning
Maxus
仗从亠亶仍 从舒亳仆仂从
仍 仂弍仍舒亳 4x4 (HR) 仂弍舒亰亠
亳亰 弍舒亰 于弍亳舒亠
仗仂 仄亳仆亳仄舒仍仆仂亶 舒弍仂仍ム仆仂亶
舒亰仆仂亳 (MAD)
仍亳 MAD 弍仂仍亠 仗仂仂亞舒,
仂 于仂从亳亠 舒仂
仆亠 亰舒仗仂仍仆ム
Jiji, C. V., and S. Chaudhuri, Single-frame image super-
resolution through ontourlet learning., EURASIP, 2006 11
CS MSU Graphics & Media Lab (Video Group)
12. Only for
Contourlet learning
Maxus
仗从亠亶仍 从舒亳仆仂从
Jiji, C. V., and S. Chaudhuri, Single-frame image super-
resolution through ontourlet learning., EURASIP, 2006 12
CS MSU Graphics & Media Lab (Video Group)
13. Only for
Contourlet learning
Maxus
亠亰仍舒
Original
Jiji, C. V., and S. Chaudhuri, Single-frame image super-
resolution through ontourlet learning., EURASIP, 2006 13
CS MSU Graphics & Media Lab (Video Group)
14. Only for
Contourlet learning
Maxus
亠亰仍舒
Contourlet, 21.81 dB
Jiji, C. V., and S. Chaudhuri, Single-frame image super-
resolution through ontourlet learning., EURASIP, 2006 14
CS MSU Graphics & Media Lab (Video Group)
15. Only for
Contourlet learning
Maxus
亠亰仍舒
Bicubic, 19.95 dB
Jiji, C. V., and S. Chaudhuri, Single-frame image super-
resolution through ontourlet learning., EURASIP, 2006 15
CS MSU Graphics & Media Lab (Video Group)
16. Only for
Contourlet learning
Maxus
亠亰仍舒
Jiji, C. V., and S. Chaudhuri, Single-frame image super-
resolution through ontourlet learning., EURASIP, 2006 16
CS MSU Graphics & Media Lab (Video Group)
18. Only for
Maxus
弌仂亟亠亢舒仆亳亠
于亠亟亠仆亳亠
Contourlet learning
Repetitive structures
Contour stencils
Softcuts
18
CS MSU Graphics & Media Lab (Video Group)
19. Only for
Maxus
Repetitive structures
亟亠 舒仍亞仂亳仄舒:
LR-亳亰仂弍舒亢亠仆亳亳
弍仗亳从亠仍仆仂亶 仂仆仂
亳 仗仂仂亢亳亠 仍亠仄亠仆
舒亢亟亶 仗亳从亠仍 HR-亳亰仂弍舒亢亠仆亳
于仂舒仆舒于仍亳于舒亠 仆舒 仂仆仂于亠
仆亠从仂仍从亳 仍亠仄亠仆仂于
LR-亳亰仂弍舒亢亠仆亳 (亠仍亳 仆舒亶亟
亟仂舒仂仆仂 仗仂仂亢亳 仍亠仄亠仆仂于)
Luong, H.Q., A. Ledda and W. Philips, An Image Interpolation
Scheme For Repetitive Structures, ICIAR 2006 19
CS MSU Graphics & Media Lab (Video Group)
20. Only for
Repetitive structures
Maxus
弍仂 仗仂仂亢亳 弍仍仂从仂于
CC cross-corelation
MAD mean absolute difference
立 亠亠亠仆仆亶 弍仍仂从
m(x) 仂从舒 从舒亳仆从亳, 仂仂于亠于ム舒 仂从亠
亠亠亠仆仆仂亞仂 弍仍仂从舒
Luong, H.Q., A. Ledda and W. Philips, An Image Interpolation
Scheme For Repetitive Structures, ICIAR 2006 20
CS MSU Graphics & Media Lab (Video Group)
21. Only for
Repetitive structures
Maxus
仂舒仆仂于仍亠仆亳亠 HR-仗亳从亠仍
仂亰仄仂亢仆亠 仍舒亳:
亠从仂仍从仂 弍仍仂从仂于 仗仂从于舒ム HR-仗亳从亠仍
(~31% 仗亳从亠仍亠亶)
亠亰仍舒 仄亠亟亳舒仆舒 仗仂 于仂亰仄仂亢仆仄 亰仆舒亠仆亳礆
HR-仗亳从亠仍 仂仂于亠于亠 LR-仗亳从亠仍
(~1,5% 仗亳从亠仍亠亶)
亠亰仍舒 LR-亰仆舒亠仆亳亠 仗亳从亠仍
亳 仂亟亳仆 弍仍仂从 仆亠 仗仂从于舒亠 HR-仗亳从亠仍
(~67,5% 仗亳从亠仍亠亶)
亠亰仍舒 亳仆亠仗仂仍亳 仗仂 LR-亳亰仂弍舒亢亠仆亳
Luong, H.Q., A. Ledda and W. Philips, An Image Interpolation
Scheme For Repetitive Structures, ICIAR 2006 21
CS MSU Graphics & Media Lab (Video Group)
22. Only for
Repetitive structures
Maxus
亠亰仍舒
Bicubic
Original
Repetitive structures
Luong, H.Q., A. Ledda and W. Philips, An Image Interpolation
Scheme For Repetitive Structures, ICIAR 2006 22
CS MSU Graphics & Media Lab (Video Group)
23. Only for
Repetitive structures
Maxus
亠亰仍舒
Nearest neighbor Bicubic Repetitive structures
Luong, H.Q., A. Ledda and W. Philips, An Image Interpolation
Scheme For Repetitive Structures, ICIAR 2006 23
CS MSU Graphics & Media Lab (Video Group)
36. Only for
Contour stencils
Maxus
仂仂亶 仗仂亟仂亟: 仗仂仂亠仆亳亠 弍亠
P. Getreuer, Image zooming with contour stencils.
Proceedings of SPIE, vol. 7246, 2009. 36
CS MSU Graphics & Media Lab (Video Group)
37. Only for
Contour stencils
Maxus
仂仂亶 仗仂亟仂亟
H 仂仗亠舒仂, 亟亠亶于ム亳亶 从舒从
仆亳亰从仂舒仂仆亶 亳仍. 丐仂亞亟舒 仂仗亠亟亠仍亳仄
仂仗亠舒仂 H, G, G 舒从亳亠, 仂 HH + GG = I
丐仂亞亟舒 HR-亳亰仂弍舒亢亠仆亳亠 仄仂亢仆仂
舒仄舒亳于舒 从舒从 u = Hv + Gd, 亳 亰舒亟舒舒
仄亳仆亳仄亳亰舒亳亳 于仂亟亳 从 仗仂亳从 d:
mindE(Hv + Gd)
37
CS MSU Graphics & Media Lab (Video Group)
38. Only for
Contour stencils
Maxus
仂仂亶 仗仂亟仂亟
弍仂亰仆舒亳 亠亠亰 L 仂仗亠舒仂 舒仗仍舒舒:
( Lu) : ワ w( , )(u u )
亠亠仆亳亠 亰舒亟舒亳 于仂亟亳 从 亠亠仆亳
亳亠仄:
(G'*LG' )d G'*LH ' v
38
CS MSU Graphics & Media Lab (Video Group)
39. Only for
Contour stencils
Maxus
仂仂亶 仗仂亟仂亟: 亠亰仍舒
LR-从舒亳仆从舒 亠于亶 仗仂亟仂亟 仂仂亶 仗仂亟仂亟
P. Getreuer, Image zooming with contour stencils.
Proceedings of SPIE, vol. 7246, 2009. 39
CS MSU Graphics & Media Lab (Video Group)
40. Only for
Contour stencils
Maxus
于仂亟
仂仂亳仆于舒:
于舒 仗仂亟仂亟舒: 仂亟亳仆 舒仂仄 仆舒 从仂仂,
亟亞仂亶 仆舒 从舒亠于仂
丱仂仂舒 亳仆亠仗仂仍亳 于亟仂仍 亞舒仆亳
仂亰仄仂亢仆仂 舒仗舒舒仍仍亠仍亳于舒仆亳 于亳仍亠仆亳亶
亠亟仂舒从亳:
亞舒仆亳亠仆亳亠 仆舒 亞亳弍从仂: 仗仂亟仂弍舒仆仆亠 于亠舒 亳
舒弍仍仂仆
P. Getreuer, Image zooming with contour stencils.
Proceedings of SPIE, vol. 7246, 2009. 40
CS MSU Graphics & Media Lab (Video Group)
41. Only for
Maxus
弌仂亟亠亢舒仆亳亠
于亠亟亠仆亳亠
Contourlet learning
Repetitive structures
Contour stencils
Softcuts
41
CS MSU Graphics & Media Lab (Video Group)
42. Only for
Maxus
SoftCuts
仍亞仂亳仄 仆舒亠仍亠仆 仆舒 舒仆亠仆亳亠 舒仍亳舒亳仆亞舒
仗亳 亳仆亠仗仂仍亳亳 于亟仂仍 亞舒仆亳
仗仂仍亰ム 亳亟亠亳 亳亰 舒仍亞仂亳仄仂于 舒亰亠亰舒
亞舒舒 亳 仄舒亳仂于舒仆亳 (matting)
亳亰仂弍舒亢亠仆亳亶
Shengyang Dai, Mei Han, Wei Xu, et al., Softcuts: A Soft Edge
Smoothness Prior for Color Image Super Resolution, 42
CS MSU Graphics & Media Lab (Video Group) IEEE T-IP, 2009
43. Only for
SoftCuts
Maxus
舒亰弍亳亠仆亳亠 仆舒 仍仂亳
亰仂弍舒亢亠仆亳亠 舒亰弍亳于舒亠 仆舒 仍仂亳 仗仂仄仂
舒仍亞仂亳仄舒 Spectral matting
弌仍仂亳 仄仂亞 仗亠亠亠从舒
舒仍亠亠 从舒亢亟亶 仍仂亶 仂弍舒弍舒于舒亠 仆亠亰舒于亳亳仄仂
(于 仂舒仍仆仂亠 亳舒亠 仂仆仂仄)
Shengyang Dai, Mei Han, Wei Xu, et al., Softcuts: A Soft Edge
Smoothness Prior for Color Image Super Resolution, 43
CS MSU Graphics & Media Lab (Video Group) IEEE T-IP, 2009
44. Only for
SoftCuts
Maxus
弌从舒 亞舒舒
亠舒 仆舒 弍舒 仂仗亠亟亠仍ム 仗仂 仂仄仍亠:
Shengyang Dai, Mei Han, Wei Xu, et al., Softcuts: A Soft Edge
Smoothness Prior for Color Image Super Resolution, 44
CS MSU Graphics & Media Lab (Video Group) IEEE T-IP, 2009
45. Only for
SoftCuts
Maxus
仂亠 仂弍舒弍仂从亳
仂亟亠仍 仗仂亠舒 仄亠仆亠仆亳
亳亰仂弍舒亢亠仆亳:
亠亟仍舒亞舒亠仄亶 仗仂亠 于仂舒仆仂于仍亠仆亳:
Shengyang Dai, Mei Han, Wei Xu, et al., Softcuts: A Soft Edge
Smoothness Prior for Color Image Super Resolution, 45
CS MSU Graphics & Media Lab (Video Group) IEEE T-IP, 2009
46. Only for
SoftCuts
Maxus
仂亠 仂弍舒弍仂从亳
仂亠 亳亠舒亳仂仆仆亶, 于 从舒亠于亠 仗亠于仂亞仂 仗亳弍仍亳亢亠仆亳
弍亠 亠亰仍舒 弍亳从弍亳亠从仂亶 亳仆亠仗仂仍亳亳.
nG - 从仂仍亳亠于仂
仆舒仗舒于仍亠仆亳亶 于磶仆仂亳
Dek 仂仗亠舒仂 亟于亳亞舒
仆舒 于亠从仂 ek
Shengyang Dai, Mei Han, Wei Xu, et al., Softcuts: A Soft Edge
Smoothness Prior for Color Image Super Resolution, 46
CS MSU Graphics & Media Lab (Video Group) IEEE T-IP, 2009
47. Only for
SoftCuts
Maxus
丶于亠仆亠 亳亰仂弍舒亢亠仆亳
仍 于亠仆 亳亰仂弍舒亢亠仆亳亶 舒仍亞仂亳仄
仍仂亢仆磳: 从舒亢亟亶 于亠仂于仂亶 从舒仆舒仍
仂弍舒弍舒于舒亠 仂亟亠仍仆仂, 仆仂 亳仗仂仍亰ム
仂弍亳亠 亟舒仆仆亠 仂弍 舒仍舒-从舒仆舒仍亠
仍 舒 舒仍舒-从舒仆舒仍舒 亟仍 从舒亢亟仂亞仂
于亠仂于仂亞仂 从舒仆舒仍舒 于 从舒亢亟仂亶 仂从亠
于亳仍磳 束舒亟舒仗亳于仆亶 舒从仂損
Shengyang Dai, Mei Han, Wei Xu, et al., Softcuts: A Soft Edge
Smoothness Prior for Color Image Super Resolution, 47
CS MSU Graphics & Media Lab (Video Group) IEEE T-IP, 2009
48. Only for
SoftCuts
Maxus
丶于亠仆亠 亳亰仂弍舒亢亠仆亳
亟舒仗亳于仆亶 舒从仂 亳仆亠仗仂仍亳亠
弍亳从弍亳亠从亳仄 仄亠仂亟仂仄, .从. 舒仍舒-从舒仆舒仍
仂弍仍舒亟舒亠 于仂亶于仂仄 亞仍舒亟从仂亳
亠亞仍亳亰舒亳 仂仄 舒仍舒-从舒仆舒仍舒:
Shengyang Dai, Mei Han, Wei Xu, et al., Softcuts: A Soft Edge
Smoothness Prior for Color Image Super Resolution, 48
CS MSU Graphics & Media Lab (Video Group) IEEE T-IP, 2009
49. Only for
SoftCuts
Maxus
亠亰仍舒
仂亟仆舒 从舒亳仆从舒 亠亰仍舒
Shengyang Dai, Mei Han, Wei Xu, et al., Softcuts: A Soft Edge
Smoothness Prior for Color Image Super Resolution, 49
CS MSU Graphics & Media Lab (Video Group) IEEE T-IP, 2009
50. Only for
SoftCuts
Maxus
亠亰仍舒
仂亟仆亳从 Bicubic Bicubic + Back- Proposed
unsharpen projection
50
CS MSU Graphics & Media Lab (Video Group)
51. Only for
SoftCuts
Maxus
弌从仂仂 舒弍仂
亠舒仍亳亰舒亳 仆舒 MATLAB, PIV 3 GHz, RAM 1GB
亰仂弍舒亢亠仆亳亠 107x160
亠仄 舒弍仂 spectral matting: 120 亠从仆亟
亠仄 舒弍仂 softcuts: 35 亠从仆亟 (30
亳亠舒亳亶)
Shengyang Dai, Mei Han, Wei Xu, et al., Softcuts: A Soft Edge
Smoothness Prior for Color Image Super Resolution, 51
CS MSU Graphics & Media Lab (Video Group) IEEE T-IP, 2009
52. Only for
SoftCuts
Maxus
于仂亟
仂仂亳仆于舒
亠仆 于仂从仂亠 于亳亰舒仍仆仂亠 从舒亠于仂
亠亟仂舒从亳
亠仆 仆亳亰从舒 从仂仂
52
CS MSU Graphics & Media Lab (Video Group)
54. Only for
Maxus
亳亠舒舒
1. Luong, H.Q., A. Ledda and W. Philips, An Image Interpolation Scheme
For Repetitive Structures., ICIAR 2006
2. Jiji, C. V., and S. Chaudhuri, Single-frame image super-resolution
through ontourlet learning, EURASIP 2006
3. P. Getreuer, Image zooming with contour stencils. Proceedings of SPIE,
vol. 7246, 2009.
4. Shengyang Dai, Mei Han, Wei Xu, et al., Softcuts: A Soft Edge
Smoothness Prior for Color Image Super Resolution, IEEE T-IP, 2009
54
CS MSU Graphics & Media Lab (Video Group)