An Image Denoising Analysis with Iterative Histogram Specification

Abstract

In any application image denoising is a challenging task because noise removal will increase the digital quality of an image and will improve the perceptual visual quality. In spite of the great success of many denoising algorithms, they tend to smooth the fine scale image textures when removing noise, degrading the image visual quality. To address this problem, in this paper we propose a texture enhanced image denoising method by enforcing the gradient histogram of the denoised image to be close to a reference gradient histogram of the original image. Given the reference gradient histogram, a novel gradient histogram preservation (GHP) algorithm is developed to enhance the texture structures while removing noise. Simulation results show that the proposed method has given the better performance when compared to the existing algorithms in terms of peak signal to noise ratio (PSNR) and mean square error (MSE). To deal with this crisis, on this paper, we endorse a texture more desirable picture denoising process through implementing the gradient histogram of the denoised image to be just about a reference gradient histogram of the long-established snapshot. Given the reference gradient histogram, a novel gradient histogram renovation (GHP) algorithm is developed to enhance the texture buildings while casting off noise. Two neighborhood-founded editions of GHP are proposed for the denoising of pictures including areas with one-of-a-kind textures. An algorithm is also developed to conveniently estimate the reference gradient histogram from the noisy remark of the unknown snapshot. Our experimental outcome display that the proposed GHP algorithm can good retain the feel looks within the denoised graphics, making them appear more normal.