Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Second-order Cone Programming Methods for Total Variation-Based Image Restoration
SIAM Journal on Scientific Computing
Extensions of compressed sensing
Signal Processing - Sparse approximations in signal and image processing
Compressive imaging of color images
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
SIAM Journal on Imaging Sciences
On compressive sensing applied to radar
Signal Processing
Compressed sensing of complex-valued data
Signal Processing
Alternating Direction Algorithms for $\ell_1$-Problems in Compressive Sensing
SIAM Journal on Scientific Computing
NESTA: A Fast and Accurate First-Order Method for Sparse Recovery
SIAM Journal on Imaging Sciences
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
An EM algorithm for wavelet-based image restoration
IEEE Transactions on Image Processing
A New TwIST: Two-Step Iterative Shrinkage/Thresholding Algorithms for Image Restoration
IEEE Transactions on Image Processing
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IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
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Total variation (TV) minimization algorithms are often used to recover sparse signals or images in the compressive sensing (CS). But the use of TV solvers often suffers from undesirable staircase effect. To reduce this effect, this paper presents an improved TV minimization method for block-based CS by intra-prediction. The new method conducts intra-prediction block by block in the CS reconstruction process and generates a residual for the image block being decoded in the CS measurement domain. The gradient of the residual is sparser than that of the image itself, which can lead to better reconstruction quality in CS by TV regularization. The staircase effect can also be eliminated due to effective reconstruction of the residual. Furthermore, to suppress blocking artifacts caused by intra-prediction, an efficient adaptive in-loop deblocking filter was designed for post-processing during the CS reconstruction process. Experiments show competitive performances of the proposed hybrid method in comparison with state-of-the-art TV models for CS with respect to peak signal-to-noise ratio and the subjective visual quality.