PROJECT TITLE :

Gradient Histogram Estimation and Preservation for Texture Enhanced Image Denoising - 2014

ABSTRACT:

Natural image statistics plays an necessary role in image denoising, and varied natural image priors, including gradient-based mostly, sparse illustration-based mostly, and nonlocal self-similarity-based mostly ones, have been widely studied and exploited for noise removal. In spite of the great success of the many denoising algorithms, they have a tendency to swish the fine scale image textures when removing noise, degrading the image visual quality. To address this downside, during this paper, we have a tendency to propose a texture enhanced image denoising technique by enforcing the gradient histogram of the denoised image to be shut to a reference gradient histogram of the first image. Given the reference gradient histogram, a completely unique gradient histogram preservation (GHP) algorithm is developed to enhance the texture structures whereas removing noise. 2 region-based mostly variants of GHP are proposed for the denoising of images consisting of regions with completely different textures. An algorithm is additionally developed to effectively estimate the reference gradient histogram from the noisy observation of the unknown image. Our experimental results demonstrate that the proposed GHP algorithm can well preserve the feel look in the denoised images, making them look additional natural.