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Kernel Regression analysis is a traditional regression analysis on the latest developments in image denoising , data mining, super-resolution analysis and other fields has been widely used. Non- local method is currently the most popular image processing method , which calculates the weight characteristics to consider the overall image , the process is much better than other methods , the disadvantage is large amount of calculation . In this paper, the National Science Foundation of Natural background , combined with the locality of kernel regression algorithm with non local algorithm overall, will be applied to image denoising , got better treatment effect . This paper first introduces the basic concepts of image processing as well as common noise model , recalling the traditional image denoising method to analyze the deficiencies of the traditional denoising methods , these methods are based on a specific model structure , since the model has been set, which limits their applications, kernel regression method a good improvement of the previous denoising these shortcomings , the disadvantage is not taken into account in the de-noising gray image information , this article by introducing local direction information to improve the classical kernel regression . Kernel regression weights in the calculation of analyzing only the pixels adjacent areas , in fact, distant region may also have some similar characteristics , so the kernel regression method has some limitations. This paper describes a non- local denoising good use of the image of global features , for a similar texture image , the method of the treatment effect is much better than other methods. However, its use is locally constant model , it is the kernel regression is equal to 0 in the order of a special case , by combining the high-level kernel regression resistance, can be a good way to improve the lack of non- local . Finally, experiments and analysis show that this method as compared with the conventional method , since a good combination of the local image features and global feature , making the image denoising better able to keep the texture and structure of the information, with better denoising effect .
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