Content-adaptive low rank regularization for image denoising

Prior knowledge plays an important role in image denoising tasks. This paper utilizes the data of the input image to adaptively model the prior distribution. The proposed scheme is based on the observation that, for a natural image, a matrix consisted of its vectorized non-local similar patches is o...

Full description

Saved in:
Bibliographic Details
Published in2016 IEEE International Conference on Image Processing (ICIP) pp. 3091 - 3095
Main Authors Hangfan Liu, Xinfeng Zhang, Ruiqin Xiong
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2016
Subjects
Bayes methods
empirical Bayes
Image denoising
Image restoration
low-rank
Noise measurement
Noise reduction
Non-local similarity
Optimization
Online AccessGet full text

Cover

More Information
Summary:Prior knowledge plays an important role in image denoising tasks. This paper utilizes the data of the input image to adaptively model the prior distribution. The proposed scheme is based on the observation that, for a natural image, a matrix consisted of its vectorized non-local similar patches is of low rank. We use a non-convex smooth surrogate for the low-rank regularization, and view the optimization problem from the empirical Bayesian perspective. In such framework, a parameter-free distribution prior is derived from the grouped non-local similar image contents. Experimental results show that the proposed approach is highly competitive with several state-of-art denoising methods in PSNR and visual quality.
ISSN:2381-8549
DOI:10.1109/ICIP.2016.7532928