Image Denoising Using Statistical Nearest Neighbors PROJECT TITLE : Statistical Nearest Neighbors for Image Denoising ABSTRACT: Non-local-means image denoising is based on processing a reference patch's neighbours. The algorithm's processing overhead can be reduced by using a small number of nearest neighbours (NN). Analysis of the denoised patch shows that sampling neighbours with the NN technique generates a bias in the data. In order to solve this problem, we offer a new neighbour collecting criterion called statistical NN (SNN). Using fewer SNNs to generate images of greater quality at a lower computational cost, our strategy surpasses the classic one in the case of both white and coloured noise. The differences between NN and SNN can be explained by examining our toy problem in greater detail. SNN is a broad concept, and it improves image quality even when applied to bilateral filtering. There is a free MATLAB programme that may be used to replicate the results of the study. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Convolutional Neural Networks for Single Image Reflection Removal Deep Variational Priors for Structure-Texture Image Decomposition
