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
High-quality motion deblurring from a single image
ACM SIGGRAPH 2008 papers
Split Bregman Algorithm, Douglas-Rachford Splitting and Frame Shrinkage
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
SIAM Journal on Imaging Sciences
Fast image recovery using variable splitting and constrained optimization
IEEE Transactions on Image Processing
Image annotation by kNN-sparse graph-based label propagation over noisily tagged web images
ACM Transactions on Intelligent Systems and Technology (TIST)
Summarizing tourist destinations by mining user-generated travelogues and photos
Computer Vision and Image Understanding
Compounded Regularization and Fast Algorithm for Compressive Sensing Deconvolution
ICIG '11 Proceedings of the 2011 Sixth International Conference on Image and Graphics
Sparsity-based image denoising via dictionary learning and structural clustering
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
ForWaRD: Fourier-wavelet regularized deconvolution for ill-conditioned systems
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Robust Tensor Analysis With L1-Norm
IEEE Transactions on Circuits and Systems for Video Technology
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Compressive Sensing Deconvolution (CS-Deconvolution) is a new challenge problem encountered in a wide variety of image processing fields. Since CS is more efficient for sparse signals, in our scheme, the input image is firstly sparse represented by curvelet frame system; then the curvelet coefficients are encoded by a structurally random matrix based CS sampling technique. In order to improve the CS-deconvolution performance, a compound variational regularization model, which combined total variation and curvelet-based sparsity prior, is proposed to recovery blurred image from compressive measurements. Given the compressive measurements, we propose a novel fast algorithm using variable-splitting and Dual Douglas-Rachford operator splitting methods to produce high quality deblurred results. Our method considerably improves the visual quality of CS reconstruction for the given number of random measurements and reduces the decoding computational complexity, compared to the existing CS-deconvolution methods.