Noise Estimation and Reduction

Noise is a major issue observed during image processing in image processing applications. These noise levels have to be predicted and after estimation get it reduced to a certain maximum declined level. We can't completely structure noise-free images but we can improve the quality of images by estimating those noises. The proposed approach is an innovative way to estimate and remove the noise found through observation during the processing of images. The principal component analysis (PCA) approach is followed to remove the noise by estimating it, this can be done by following one of the statistical techniques which are frequently used in signal processing for data dimension reduction or for data correlation. In the principal component analysis, image blocks were rearranged into vector and compute the covariance matrix of this vector. Then by selecting the covariance matrix eigenvalues which correspond only to noise. With the help of the average of the eigenvalues we are able to estimate the noise present in the image, for estimation of noise in the image we just take a partial region of the image so that it will be convenient for us to reduce it by using the denoise function.