Research On Image Denoising Based On Image Quality Assessment And Non-local Means

As one of the methods of image preprocessing,image denoising plays an important role in the field of image processing.Non-local means based image denoising algorithm is the focus of the study of image denoising.Image quality assessment is a method to evaluate the visual quality of the image.Image denoising algorithm based on non-local means and image quality assessment is studied in this paper.In the case of image denoising based on image self similarity,the Euclidean distance is generally used to perform the similarity measure between image blocks.Some objective image quality assessment methods not only consider the image pixels,but also consider the image brightness,contrast,structure and other information.Comparing with considering the difference of pixel values of image blocks by only using Euclidean distance,this thesis studies the method of combining objective image quality assessment methods with Euclidean distance to find the similar image blocks,and the found similar image blocks would be more similar in structure and other image information.Natural images usually contain a variety of nosies,which are difficult to be distinguished.The Gaussian distribution model is usually used to simulate the image distribution model and find the similar blocks,But the Gaussian distribution model is not necessarily suitable for mixture noises,therefore,it is essential to consider the adaptive situation of image distribution model.This thesis also studies the method of image denoising based on adaptive soft threshold,and through experimental comparision,the proposed method has achieved better performance.