An Algorithm for Total Variation Minimization and Applications
Journal of Mathematical Imaging and Vision
Adapted Total Variation for Artifact Free Decompression of JPEG Images
Journal of Mathematical Imaging and Vision
A Truncated Lagrange Method for Total Variation-Based Image Restoration
Journal of Mathematical Imaging and Vision
Image feature enhancement based on the time-controlled total variation flow formulation
Pattern Recognition Letters
Colour, texture, and motion in level set based segmentation and tracking
Image and Vision Computing
Image and Vision Computing
Equivalence results for TV diffusion and TV regularisation
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
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
Numerical Methods for the Vector-Valued Solutions of Non-smooth Eigenvalue Problems
Journal of Scientific Computing
Adaptive medical image denoising using support vector regression
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Constrained total variation minimization and application in computerized tomography
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
A four-pixel scheme for singular differential equations
Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
A linearly convergent first-order algorithm for total variation minimisation in image processing
International Journal of Bioinformatics Research and Applications
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The minimization of the total variation is an important tool of image processing. A lot of authors have addressed the problem and developed algorithms for image denoising. In this paper we present an alternative approach of the total variation minimization problem. After an introduction to the topic and a review of related work, we give a short development of the bounded variation (BV) background. Then we present our global total variation minimization model and proof its validity. Furthermore we introduce a practical algorithm which handles digital image data and we give experimental results.