Region-Aware Image Denoising by Exploring Parameter Preference

Abstract

The goal of an image denoising algorithm is to preserve the details of clean images while reducing the noise in noisy images. Some existing image denoising algorithms preserve the details by using external information. However, external information needs to be obtained from external images or regions similar to the noisy images or regions. In this letter, we propose a region-aware image denoising algorithm (RAID) by exploring parameter preference. The proposed RAID algorithm is based on the observation that different regions of noisy images prefer denoised results that differ due to being obtained with different denoising parameters. The RAID algorithm, first, measures the extent of preferences of denoising parameters for different image regions. Then, it combines the denoised results obtained by using the various denoising parameters according to the extent of preferences determined in the previous step to get the final denoised image. Experimental results show that the proposed RAID algorithm can be combined with some existing image denoising algorithms to improve their denoising performance. Our denoised results are better at preserving the details while reducing the noise than existing algorithms which use the same parameter for the whole image.