Image Inpainting | Orthogonal Matching Pursuit (OMP)| DCT Dictionary | Sparse Image Recovery| python
Image Inpainting by solving the L0 problem with Greedy sparse approximation algorithm Orthogonal Matching Pursuit (OMP) using DCT Dictionary with python
overlapping 8x8 image patches are computed (vectorized as b and stacked into matrix) and for each patch OMP is run to compute a sparse representation of the patch by using at most 10 atoms (to construct matrix A) from the (precomputed fixed) DCT dictionary D (typically overcomplete, containing 256 or 1024 atoms), with residual thresholding as a stopping criterion for the iterative OMP along with sparsity
OMP iteratively selects the best matching (correlating) atom with a given (masked) patch, adds the atom to the set of atoms selected so far in A and projects the patch on A, computes the residual from b, repeats reconstruction of the residual until the stopping criteria reached.
Different (overcomplete) DCT dictionaries (D) are used for reconstruction (e.g., with 16^2=256 and 32^2=1024 atoms)
inpainting mask is used to discard the (unknown) pixels (shown in red) from the given corrupted image and the rest of the pixels from the image patches are used in OMP
#imageprocessing #imageprocessingpython #python #computervision #algorithm #machinelearning #optimization #sparse #compressedsensing
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