Atomic Decomposition by Basis Pursuit
SIAM Review
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
Non-local Regularization of Inverse Problems
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Model-Guided Adaptive Recovery of Compressive Sensing
DCC '09 Proceedings of the 2009 Data Compression Conference
Exploiting structure in wavelet-based Bayesian compressive sensing
IEEE Transactions on Signal Processing
Model-based compressive sensing
IEEE Transactions on Information Theory
Bregmanized Nonlocal Regularization for Deconvolution and Sparse Reconstruction
SIAM Journal on Imaging Sciences
Wavelet-based statistical signal processing using hidden Markovmodels
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Decoding by linear programming
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
IEEE Transactions on Image Processing
A new, fast, and efficient image codec based on set partitioning in hierarchical trees
IEEE Transactions on Circuits and Systems for Video Technology
Model-Assisted Adaptive Recovery of Compressed Sensing with Imaging Applications
IEEE Transactions on Image Processing
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Compressive sensing (CS) theory dictates that a sparse signal can be reconstructed from a few random measurements. An important issue of compressive image recovery (CIR) is that the optimal sparse space is usually unknown and/or it often varies spatially for non-stationary signals (e.g., natural images). In this paper, apart from fixed sparse spaces, prior models, specifically a set of piecewise autoregressive (AR) models that encode the common statistics of image micro-structures, are learned from example image patches, and they are then used to construct adaptive sparsity regularizers for CIR. Furthermore, a complementary non-local structural sparsity regularizer is also incorporated into the CIR process to improve the robustness. The regularization by local AR model and non-local redundancy makes the proposed CIR very effective. Experimental results on benchmark images validate that the proposed algorithm can outperform significantly previous CIR methods in terms of both PSNR and visual quality.