A convex adaptive total variation model based on the gray level indicator for multiplicative noise removal

DOI10.1155/2013/912373zbMath1470.94012OpenAlexW2006786206WikidataQ58917714 ScholiaQ58917714MaRDI QIDQ2319248

Boying Wu, Zhichang Guo, Gang Dong

Publication date: 16 August 2019

Published in: Abstract and Applied Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2013/912373

Mathematics Subject Classification ID

Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

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Cites Work

• Unnamed Item
• Unnamed Item
• Nonlinear total variation based noise removal algorithms
• Analysis of a new variational model for multiplicative noise removal
• An evolution problem for plastic antiplanar shear
• An algorithm for mean curvature motion
• A Fast Total Variation Minimization Method for Image Restoration
• A Nonlinear Inverse Scale Space Method for a Convex Multiplicative Noise Model
• A New Total Variation Method for Multiplicative Noise Removal
• A Variational Approach to Removing Multiplicative Noise
• An evolution problem for linear growth functionals
• Minimization Problems and Associated Flows Related to Weighted p Energy and Total Variation
• Computing Optical Flow via Variational Techniques
• Variable Exponent, Linear Growth Functionals in Image Restoration
• Multiplicative Noise Removal Using L1 Fidelity on Frame Coefficients