Multi scale detail-preserving denoising method of infrared image via relative total variation

Abstract

How to remove the noise in infrared image effectively with detail preserving is a significant but difficult problem in infrared image processing. Various methods have been proposed to obtain good results. However, these algorithms usually cannot distinguish noise and detail efficiently, which leads to smoothing some details in infrared images. Recently a novel local measure called relative total variation (RTV) is proposed to accomplish effective texture removal. RTV measure is combined with a general windowed total variation measure and a novel inherent variation measure to smooth the image texture effectively while preserving the main structure. In this paper, using detail preserving smoothing method via RTV, a multi scale denoising algorithm for infrared image is proposed. Firstly, the infrared image is decomposed into several scales by non-subsampled Contourlet transform (NSCT). NSCT decomposition does not do any down sampling or up sampling, thus the results are not band limited. Secondly，the algorithm applies RTV based detail preserving denoising method for each decomposed layers. Different smoothing parameters are respectively used to adjust the denoising levels in different scales. Finally, various synthetic weights are utilized to different layers to reconstruct the final infrared denosing results. Compared with other infrared denoising approaches, the quantitative comparisons demonstrate that the proposed method could well suppress the noise of infrared image while preserving the edge details effectively. Both visual quality and objective measure results show that this method is efficient and has a good application in infrared image denoising.