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Variational image deringing using varying regularization parameter

  • Representation, Processing, Analysis and Understanding of Images
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Abstract

In this paper we propose a new variational image deringing method. This method is based on total variation minimization with an adaptively varying regularization parameter. The proposed approach improves visual quality of resulting images by preserving more structural information comparing to existing methods. Attendant parallel algorithms have been developed and implemented on GPU.

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Authors and Affiliations

  1. Laboratory of Mathematical Methods of Image Processing, Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Moscow, Russia

    I. T. Sitdikov & A. S. Krylov

Authors
  1. I. T. Sitdikov
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  2. A. S. Krylov
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Correspondence to I. T. Sitdikov.

Additional information

This paper uses the materials of a report that was submitted at the 11th International Conference Pattern Recognition and Image Analysis: New Information Technologies that was held in Samara, Russia on September 23–28, 2013.

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Iskander Talgatovich Sitdikov (born 1991), Graduated from the Faculty of Computational Mathematics and Cybernetics in 2013, Lomonosov Moscow State University (MSU). He is a PhD student at the Faculty of Computational Mathematics and Cybernetics, MSU. His main research interests lie in mathematical methods of image processing and computer vision, parallel computations, and optimization theory.

Andrei Serdzhevich Krylov (born 1956). Graduated from the Faculty of Computational Mathematics and Cybernetics in 1978, Lomonosov Moscow State University (MSU). Received the degree of PhD in 1983, the degree of Dr. Sci. in 2009. He is professor and head of the Laboratory of Mathematical Methods of Image Processing at the Faculty of Computational Mathematics and Cybernetics, MSU. His main research interests lie in mathematical methods of multimedia data processing and analysis.

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Sitdikov, I.T., Krylov, A.S. Variational image deringing using varying regularization parameter. Pattern Recognit. Image Anal. 25, 96–100 (2015). https://doi.org/10.1134/S1054661815010186

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  • DOI: https://doi.org/10.1134/S1054661815010186

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