The 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 oneof-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.