A new approach to the maximum-flow problem
Journal of the ACM (JACM)
The JPEG still picture compression standard
Communications of the ACM - Special issue on digital multimedia systems
Nonlinear total variation based noise removal algorithms
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Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
JPEG Still Image Data Compression Standard
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Segmentation by Grouping Junctions
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Image Restoration with Discrete Constrained Total Variation Part I: Fast and Exact Optimization
Journal of Mathematical Imaging and Vision
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L0-Norm and Total Variation for Wavelet Inpainting
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
A fast approach for overcomplete sparse decomposition based on smoothed l0 norm
IEEE Transactions on Signal Processing
A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
SIAM Journal on Imaging Sciences
Total variation minimization and graph cuts for moving objects segmentation
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Total variation minimization and a class of binary MRF models
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
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IEEE Transactions on Signal Processing
Exact optimization for Markov random fields with convex priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image decomposition via the combination of sparse representations and a variational approach
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
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In this paper, we deal with l 0-norm data fitting and total variation regularization for image compression and denoising. The l 0-norm data fitting is used for measuring the number of non-zero wavelet coefficients to be employed to represent an image. The regularization term given by the total variation is to recover image edges. Due to intensive numerical computation of using l 0-norm, it is usually approximated by other functions such as the l 1-norm in many image processing applications. The main goal of this paper is to develop a fast and effective algorithm to solve the l 0-norm data fitting and total variation minimization problem. Our idea is to apply an alternating minimization technique to solve this problem, and employ a graph-cuts algorithm to solve the subproblem related to the total variation minimization. Numerical examples in image compression and denoising are given to demonstrate the effectiveness of the proposed algorithm.