An effective method for signal extraction from residual image, with application to denoising algorithms

Authors:
Min-Xiong Zhou;Xu Yan;Hai-Bin Xie;Hui Zheng;Guang Yang
Affiliations:
Shanghai Key Laboratory of Magnetic Resonance, Physics Department, East China Normal University, Shanghai, China;Shanghai Key Laboratory of Magnetic Resonance, Physics Department, East China Normal University, Shanghai, China;Shanghai Key Laboratory of Magnetic Resonance, Physics Department, East China Normal University, Shanghai, China;Shanghai Key Laboratory of Magnetic Resonance, Physics Department, East China Normal University, Shanghai, China;Shanghai Key Laboratory of Magnetic Resonance, Physics Department, East China Normal University, Shanghai, China
Venue:
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Year:
2013

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Abstract

To minimize image blurring and detail loss caused by denoising, we propose a novel method to exploit residual image. Firstly, we apply Non-local Means (NLM) filter to original image to get the denoised image and store the weights used for averaging. Secondly, we filter the residual image with the stored weights. Then a Gaussian filter is applied to the denoised residual image before we add the results to image denoised by NLM to recover the lost image details. Different from previous methods, our method uses the structure information in the original image and can be used to extract lost image details from residual images with very low SNR. An analysis on the mechanism of the signal extraction method is given. Quantitative evaluation showed that the proposed algorithm effectively improved accuracy of NLM filter. In addition, the residual of the final results contained fewer observable structures, demonstrating the effectiveness of the proposed method to recover lost details.