Post on 21-Jun-2015
description
Signals, Images and More
Advanced Computational Signal/Image Processing
This course:
www.wavelet-tour.com
sounds videos 3D meshes
→ 25% mathematical theory (filtering, wavelets, regularization, . . . ).
(compression, inpainting, super-resolution, segmentation, . . . ).→ lots of problems
→ needs mathematical modeling and fast processing algorithms.
→ 25% fast numerical algorithms
→ lots of different datas (sounds, images, videos, meshes, . . . ).
→ 50% practical implementation on real applications (Scilab / Matlab).
(wavelet transform, gradient methods, . . . ).
JPEG compression:
JPEG-2000 compression:
→ uses local Fourier transform.
→ blocking artefacts.
→ uses wavelet transform.
→ better compression.
Wavelet CompressionEnter Wavelets…
• Standard 2-D tensor product wavelet transform
Image f JPEG, R = .19bit/pxl JPEG2k, R = .15bit/pxl
JPEG Compression
256x256 pixels, 12,500 total bits, 0.19 bits/pixel
JPEG Compression
256x256 pixels, 12,500 total bits, 0.19 bits/pixel
EZW Compression
256x256 pixels, 9,800 total bits, 0.15 bits/pixel
2D wavelets
Noise in Images
Wavelet thresholdingDenoising using
Inpainting: recovering missing information.
Compressed sensing: designing more efficients sensors.→ use randomized and delocalized measurements.
→ interpolate the image.
Inverse Problems