Image Restoration
Key words: iterative methods, POCs, robust estimation, sparsity Our research interests in inverse problems cover recovery from: highly distorted images that include blur and mixture noise originating from several distributions missed samples (non-uniform or sparse sampling) in both coordinate and Fourier domains quantized sparse sampling optimisation techniques suitable for high dimensional data machine-learning based methods
Image denoising
Key words: robust estimation, sparsity, edge process model Gaussian noise Misssed samples Robust estimators POCS
Face image compression
Key words: codebook learning, successive refinement, rate-distortion, polynomial complexity We study several schemes for multi-layer representation of images. The problem is first treated from an information-theoretic viewpoint where we analyze the behavior of different sources of information under a multi-layer data compression framework and compare it with a single-stage (shallow) structure. We then consider the image data as the source of information and link the proposed representation scheme to the problem of multi-layer dictionary learning for visual data. For the current work we focus on the problem of image compression for a special class of images where we report a considerable performance boost in terms of PSNR at high compression ratios in comparison with the JPEG2000 codec.
Sparse antenna arrays and radio interferometry imaging
