A Faster Patch Ordering Method for Image Denoising

TL;DR

The paper introduces a faster patch ordering method for image denoising by using smaller TSPs, significantly reducing computation time while maintaining comparable denoising quality.

Contribution

It proposes a modified patch ordering approach that uses smaller TSPs, decreasing processing time by 40% with minimal impact on denoising performance.

Findings

Denoising results differ by only 0.016 to 0.032 dB in PSNR.

TSP solving time is reduced to half of the original method.

Overall denoising process is 40% faster.

Abstract

Among the patch-based image denoising processing methods, smooth ordering of local patches (patch ordering) has been shown to give state-of-art results. For image denoising the patch ordering method forms two large TSPs (Traveling Salesman Problem) comprised of nodes in N-dimensional space. Ten approximate solutions of the two large TSPs are then used in a filtering process to form the reconstructed image. Use of large TSPs makes patch ordering a computationally intensive method. A modified patch ordering method for image denoising is proposed. In the proposed method, several smaller-sized TSPs are formed and the filtering process varied to work with solutions of these smaller TSPs. In terms of PSNR, denoising results of the proposed method differed by 0.032 dB to 0.016 dB on average. In original method, solving TSPs was observed to consume 85% of execution time. In proposed method, the time for solving TSPs can be reduced to half of the time required in original method. The proposed method can denoise images in 40% less time.