Digital Image Denoising Algorithm Research Based On Wavelet With Irregular Neighborhood

Image denoising is an important part of image processing, it has significant effects on further image analysis. NeighShrink algorithm proposed by G.Y.Chen has great advantages in preserving image details. It estimates the coefficients with the neighborhood around them. However, through the research, it's found that NeighShrink algorithm just chooses the coefficients in a fixed windows around the thresholded one, so the edge informations will be over-smoothed. Choosing a neighborhood reasonably is helpful to make better use of the wavelet coefficients relativity in the same scale, in this way it's can be achieved to reduce the Gibbs phenomenon and protect the edge of the images. In this paper, sevral algorithms are proposed to select the reasonable neighborhoods to improve the NeighShrink algorithm.1. An adaptive neighborhood image denoising algorithm based on relativity analysis in wavelet domain is proposed. It chooses neighborhood window by the mean of correlation coefficent in neighborhood with different size, which follows the neighborhood relativity of wavelet coefficients better. The experiments results show that the image can be denoised well while preserving the edge feature.2. For NeighShrink method used in the image denoising, a new image de-noising algorithm is proposed to keep image edges more effectively, and it mainly improves the domain of NeighShrink which is fixed. This method decomposes noisy images with stationary wavelet transform to keep phase invariance. Then it segments the low frequency sub-band into many domains adaptively using PCNN model, and mapped this segmentation information to all the high frequency subbands. Then it combinates with wavelet intra-scale relativity, gets various irregular neighborhood with a fixed window, selects the data again. And the edge information is protected during the denoising process.3. An effective denoising method in edge protecting was proposed to overcome the limitation of image denosing methods now, which combines edge detection with image denoising method in wavelet domain. This algorithm decomposes noisy images using stationary wavelet transform to keep phase invariance. Then it detects the edges of low frequency subband and high frequency ones, and gets the approximate information of the edge of origin image by fusion the results of edge detection. The new neighborhood is gotten by the edge information segmentation. Based on intra-scale relativity and inter-scale relativity of wavelet coefficients,the proposed method performs denoising with the new neighborhood weighed. A better restoration of image is demonstrated in the results of experiments, with detail of images kept as well as image noises decreasing.