Nonlocal Means Denoising Algorithm Based on DCT Subspace

Nonlocal means(NLM) has been becoming one of the most useful tools for image denoising.However,the computational cost is high due to the fact that calculation for similarity weights is performed in a full space of neighborhood patches.In addition,the calculation for its similarity weights has limited accuracy against noise when the noise standard deviation is large.In order to handle above-mentioned problems,we introduce a novel image denoising algorithm that integrates discrete cosine transform(DCT) into the NLM method,motivating from promising characteristics of DCT,such as low data correlation and high energy compaction,etc.Our novel NLM-DCTS algorithm is capable of improving quality and reducing the computational cost.Compared with NLM algorithm,the PSNR value can be improved more than 1 dB by NLM-DCTS,and the running time can be reduced to 1/10 of NLM's.