Pyramid-based texture analysis/synthesis
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Reconstruction of Wavelet Coefficients Using Total Variation Minimization
SIAM Journal on Scientific Computing
Total Variation Wavelet Inpainting
Journal of Mathematical Imaging and Vision
A fast optimization transfer algorithm for image inpainting in wavelet domains
IEEE Transactions on Image Processing
Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing
Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Region filling and object removal by exemplar-based image inpainting
IEEE Transactions on Image Processing
Image coding using wavelet transform
IEEE Transactions on Image Processing
A Primal–Dual Method for Total-Variation-Based Wavelet Domain Inpainting
IEEE Transactions on Image Processing
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Loss of information in wavelet transform domain may occur while transmitting images represented in JPEG 2000. This produces artifacts like black holes, degraded edges and correlated damage patterns in received images. Recovering an image from its partial wavelet coefficients or filling the missing wavelet coefficients from the available coefficients is called wavelet inpainting. This problem is closely related to image inpainting but here inpainting is done in wavelet domain. Mathematically it is equivalent to solving a set of under determined system of equations having infinite number of solutions. In this paper, we propose an efficient algorithm that uses greedy Orthogonal Matching Pursuit (OMP) algorithm that solves the under determined problem by minimizing the l0 norm of the sparse Discrete Cosine Transform (DCT) representation of the image. The image is reconstructed by taking inverse DCT. We show that good quality reconstruction can be obtained even if 50 % of the randomly selected wavelet coefficients including the approximation coefficients are missing. The proposed method outperforms the gradient based and optimum transfer function based inpainting algorithms in terms of SNR.