MDL context modeling of images with application to denoising

Authors:
Guangtao Zhai;Xiaolin Wu;Xiaokang Yang;Weyun Zhang
Affiliations:
Department of Electrical & Computer Engineering, McMaster University, Hamilton, Ontario, Canada and Institute of Image Communication and Information Processing, Shanghai Jiao Tong University, ...;Department of Electrical & Computer Engineering, McMaster University, Hamilton, Ontario, Canada;Institute of Image Communication and Information Processing, Shanghai Jiao Tong University, Shanghai, China;Institute of Image Communication and Information Processing, Shanghai Jiao Tong University, Shanghai, China
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

The lately popularized patch-based nonlocal (NL) image processing approach is cast into a framework of statistical context modeling, a thoroughly studied topic in data compression and information theory. The adaptation of imate patch (context) to local waveform is crucial to the performance of NL-type of image processing but yet lacks a rigorous study. In this paper we propose a minimum description length (MDL) approach for choosing the size and spatial configuration of the context in which a degraded pixel is to be restored. The MDL criterion of context formation aims to strike an optimal balance between the variance and bias of the errors in fitting a 2D piecewise autoregressive (PAR) model to input image signal. To exemplify the use of the proposed context modeling technique in image processing, an MDL-guided context-based image denoiser is derived and its performance evaluated. Empirical results show that the new context-based denoiser is highly competitive against the current state of the art.