Enhancing Denoised Image Via Fusion With a Noisy Image

Jun-Sang Yoo, Jong-Ok Kim

Abstract: Image denoising unintendedly removes the original information as well as noises. Especially, texture tends to be easily distorted and smoothed by denoising because it is not distinguishable from noise. In this paper, we propose a novel framework to enhance the denoised image. The lost information of the denoised image is restored by fusing it with a noisy input. The proposed fusion is done by cost optimization which includes two data terms (noisy and denoised), and sparsity constraint term which is adopted to effectively suppress the noise in the principal component analysis (PCA) domain. The fusing weight between noisy and denoised significantly depends on the local region characteristics. PCA coefficient and eigenvector are estimated in a alternate way, and are used for estimating the enhanced version. Experimental results show that the proposed method convincingly improve texture and structural information for an image.