Structure Preserved Image Denoising Algorithms

An image is usually corrupted by noise in its acquisition and transmission. The degraded image severely affects the following image processing, such as image superresolution, image segmentation, image recognition, feature extraction. Thus, image denoising becomes a fundamental and important image processing for improving the quality of image. The goal of image denoising is to get a clearer and richer detailed image. Preserving the important structures such as edges and textures has important theoretical significance and application values.It is well-known that image structures are important for visual perception.Hence; this paper mainly focuses on image denoising model for edge-preservation and texture-preservation. Several new image denoising methods are proposed based on wavelet transform, kernel regression and nonlocal means, respectively. The main achievements and innovations are as follows:(1) Using the scale correlation and maxima modulus of wavelet coefficients, we define two new wavelet correlation coefficients based on maxima child nodes, and propose a new scale correlation based image denoising method. Furthermore; we extend these definitions to fractional B-spine wavelet coefficients. Both theoretical and experimental results demonstrate that the proposed correlation coefficients can capture the structure information in high frequency sub bands. So more edges and textures are kept in the denoised image and the quality of denoised image is improved.(2) To overcome the shortcomings of the regularity exponent based image denoising model, a new image denoising model combing the regularity exponent and image total variation (TV) is proposed. The model fully utilizes the relationship between wavelet coefficients and signal regularity. So the image regularity is modified by changing the wavelet coefficients in different scales. The noise is reduced while sharp edges are preserved; meanwhile, the Gibbs phenomenon is disappeared.With the fractional B-spline wavelet instead of the traditional wavelet, we establish a denoising algorithm based on the fractional B-spline wavelet and total variation. In this case, edges and textures are both maintained in the denoised image.(3) An adaptive kernel regression model based on structure tensor is proposed. The structure tensor, which can exploit the local gradient structure information, provides information to achieve a data-adaptive kernel function. Due to the accurate estimation of edge orientations, edges and textures structure information are well preserved and a better visual effects are achieved during denoising and interpolation. Meanwhile, the RMSE is also proved the effectiveness of the algorithm.To overcome the shortcomings of Steering kernel regression, two more robust kernel functions are applied to kernel regression, which are robust to micro-edges. On the image edges, the kernel functions have a faster decay, and the weight of pixels are assigned a small value. Thus it discourages the pseudo-edges. Experimental results of image denoising and interpolation show that the efficiency of the proposed models, especially to the image with less textures and more edge details.(4) We propose a novel nonlocal TV variation model, where the fidelity term is based on the Patch similarity, and regularity term is nonlocal TV priori. The iterative nonlocal provides structure similarity between noisy and denoised images; while the nonlocal TV preserves the edge and texture details. Compared with other related denoising methods, the proposed model can preserve more structure information in denoised image, especially to the image with much noise.(5) Combining the nonlocal Patch similarity regularization with TV regularization, we propose a new nonlocal Patch self-similarity regularized image denoising model. The similarity of Patches are said to be accurate, by introducing adaptive structure tensor to compute weight function of nonlocal Patch similarity. So in the denoised image, more structural features can be retained. A simpler and more effective algorithm, Split Bregman algorithm, is used to solve the model iteratively. By extending the model to image restoration, our model improves the quality of restoration image and the efficiency of computational complexity.