Signal Processing Course : Presentation of the Course
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This document discusses an advanced computational signal/image processing course that covers mathematical theory, fast numerical algorithms, and practical applications. The course consists of 25% mathematical theory such as filtering, wavelets, and regularization, 25% fast numerical algorithms like wavelet transforms and gradient methods, and 50% practical implementation on real applications using Scilab/Matlab. The course covers topics like signal and image compression, denoising, inpainting, and compressed sensing.
Signal Processing Course : Presentation of the Course
- 1. Signals, Images and More sounds videos 3D meshes Advanced Computational Signal/Image Processing → lots of different datas (sounds, images, videos, meshes, . . . ). → lots of problems (compression, inpainting, super-resolution, segmentation, . . . ). → needs mathematical modeling and fast processing algorithms. This course: → 25% mathematical theory (filtering, wavelets, regularization, . . . ). → 25% fast numerical algorithms (wavelet transform, gradient methods, . . . ). → 50% practical implementation on real applications (Scilab / Matlab). www.wavelet-tour.com
- 2. er Wavelets…Wavelet Compression JPEG compression: → uses local Fourier transform. → blocking artefacts. JPEG-2000 compression: → uses wavelet transform. → better compression. Compression JPEG Compression EZW Compression2D wavelets nsform Image f JPEG, R = .19bit/pxl JPEG2k, R = .15bit/pxl
- 3. Noise in Images Denoising using Wavelet thresholding
- 4. Inverse Problems Inpainting: recovering missing information. → interpolate the image. Compressed sensing: designing more efficients sensors. → use randomized and delocalized measurements.