Image denoising is an important image processing task, both as a process itself, and as a component in other processes. The main properties of a good image denoising model are that it will remove noise while preserving edges. Traditionally, linear models have been used. One common approach is to use a Gaussian filter, 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 we compare two methods in this paper. The Nonlocal Hierarchical Dictionary Learning using Wavelet (NHDLW) and Gradient Histogram Preservation (GHP),which is large success in denoising. Experimental result shows that the NHDLW get significantly better denoising results especially on an image denoising algorithms on higher noise levels.

Image denoising has great significance in pre-processing step of imaging applications. Although state-of-the-art denoising methods are numerically notable and approach theoretical limits, they suffer from visible artifacts. The image denoising methods are transformed in both spatial and transformed frequency domain. Each domain has its advantages and shortcomings, which can be complemented by each other. We propose the Progressive gradient Histogram Preservation Image Denoising (PGHP) that combine both domains. This is a simple physical process, which progressively reduces noise by texture enhanced image denoising method of enforcing the gradient histogram preservation. The results with approx 1.08% improved are pointed out from the simulation.