Research Of Semi Local Block Matching Image Denoising Algorithm Based On Principal Component Analysis

Image is an important means of accessing to, expressing and passing information, as a foundation for human perception of world. However, images will be polluted inevitably by noise in the collection and transmission. So it is necessary to deal with the noise of image. Under the good retention of the details information structure of image, a good denoising algorithm is built at foundations of achieving good denoising effect. In recent research of image denoising algorithms, some algorithms are built at the inherent model of noise, which ignore detail information of the image structure, or cause some visual illusion.Big redundancy in the image is well used by nonlocal average algorithm, which estimate of the value of the noise through search similar pixel domain in the middle of the image to match the current block. But in nonlocal algorithms, the large amount of calculation is a major problem in the matching process of the block, so the computing complexity hinder its actual application. In this paper, a new half local block matching method is built to reduce the complexity of the calculation and improve the operation efficiency of the algorithm.In order to protect fine structure information of image, building a denoising method based on principal component in this paper. The principal component analysis (PCA) is a multivariate statistical method, which selects less important variable multiple variables through linear transformation, namely the main information of images is expressed by a few little base in image-processing. according to the distribution rule of signal-to-noise ratio and the threshold shrinkage on PCA domain, image reconstruction is rebuilt by combining with the block matching method to achieve the goal of denoising. This method is very good to preserve the detail of the image to achieve a better denoising effect.