Performance evaluation of wavelet, ridgelet, curvelet and contourlet transforms based techniques for digital image denoising

Digital images always inherit some extent of noise in them. This noise affects the information content of the image. Removal of this noise is very important to extract useful information from an image. However noise cannot be eliminated, it can only be minimized due to overlap between the signal and noise characteristics. This paper reviews image denoising algorithms which are based on wavelet, ridgelet, curvelet and contourlet transforms and benchmarks them based on the published results. This article presents the techniques, parameters used for benchmarking, denoising performance on standard images and a comparative analysis of the same. This paper highlights various trends in denoising techniques, based on which it has been concluded that a single parameter Peak Signal to Noise Ratio (PSNR) cannot exactly represent the denoising performance until other parameters are consistent. A new robust parameter Performance measure 'P' is presented as a measure of denoising performance on the basis of a new concept named Noise Improvement Rectangle followed by its analysis. The results of the published algorithms are presented in tabular format in terms of PSNR and P which facilitates readers to have a bird's eye view of the research work in the field of image denoising and restoration.