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
Dictionary learning algorithms for sparse representation
Neural Computation
$\Gamma$-Convergence of Discrete Functionals with Nonconvex Perturbation for Image Classification
SIAM Journal on Numerical Analysis
Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations (Applied Mathematical Sciences)
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Journal of Mathematical Imaging and Vision
Efficient Reconstruction of Piecewise Constant Images Using Nonsmooth Nonconvex Minimization
SIAM Journal on Imaging Sciences
A New Total Variation Method for Multiplicative Noise Removal
SIAM Journal on Imaging Sciences
The Split Bregman Method for L1-Regularized Problems
SIAM Journal on Imaging Sciences
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Journal of Mathematical Imaging and Vision
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Journal of Mathematical Imaging and Vision
Homogeneity similarity based image denoising
Pattern Recognition
Multiplicative noise removal using variable splitting and constrained optimization
IEEE Transactions on Image Processing
Fast nonconvex nonsmooth minimization methods for image restoration and reconstruction
IEEE Transactions on Image Processing
A weberized total variation regularization-based image multiplicative noise removal algorithm
EURASIP Journal on Advances in Signal Processing
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This paper focuses on the problem of speckle noise removal. A new variational model is proposed for this task. In the model, a nonconvex regularizer rather than the classical convex total variation is used to preserve edges/details of images. The advantage of the nonconvex regularizer is pointed out in the sparse framework. In order to solve the model, a new fast iteration algorithm is designed. In the algorithm, to overcome the disadvantage of the nonconvexity of the model, both the augmented Lagrange multiplier method and the iteratively reweighted method are introduced to resolve the original nonconvex problem into several convex ones. From the algorithm, we can obtain restored images as well as edge indicator of the images. Comprehensive experiments are conducted to measure the performance of the algorithm in terms of visual evaluation and a variety of quantitative indices for the task of speckle noise removal.