Spatial Adaptive Filter Methods And Theirs Applications In The Tilting Mode Satellite Image Restoration

Computer technologies have moved the wheel of history into the ambitious information age. With the developments of the age, the needs of scientific researches and practical applications for high quality signals are becoming more evident. Thus, advanced digital signal technologies have gained wide interests. In the field of digital processing, filtering is a most important research subject, on the one hand, it can be able to suppress noise effectively, and on the other hand, the study of filtering can promote solution of other signal restoration problems due to that filtering is very closely associated with regularization method and image modeling theory in the terms of theory.This paper mainly focuses on the studies of the spatial adaptive filtering theoretic methods and theirs applications. In the terms of theoretic methods, we have respectively studied methods of designing adaptive fidelity coefficients, tensor-driven curvature preserving PDE based image filtering methods, preselection based non-local means image filtering methods and anisotropic diffusion PDE based structure tensor smoothing methods; In the terms of applications, we have respectively studied restoration framework and deblurring methods for tilting mode satellite image. Main innovation theory and research results have been proposed as following:1) A local structure information based method for designing the adaptive fidelity coefficients in TV model is proposed. At first, informed methods for computing the adaptive fidelity coefficients are systematically discussed and pointed out that they were trying to search local structure descriptors in essence. Then we present two requirements of local structure descriptors, which are robustness and high accuracy, and indicate that nonlinear structure tensor is an excellent local structure descriptor. After this the adaptive fidelity coefficients are constructed by nonlinear structure tensor. Experimental results show that the TV filtering method with our new adaptive fidelity coefficients is capable of sufficiently preserving geometric information such as edges and corners in addition to its effectiveness for image filtering meanwhile the speed of filtering is fast.2) A weighted curvature-preserving PDE based filtering method is proposed. At first, informed tensor-driven PDE based filtering methods are systematically analyzed. Then, the tensor-driven curvature-preserving PDE filtering method is pointed out that it can not preserve image edge very well due to that the method did not take into account the differences between integral curves. Based on this, we employ local image directional information to design weight coefficients for different integral curve, and present a new tensor-driven curvature-preserving PDE. Experimental results indicate that new method shows superior performance on preserving image edge and curvature geometric structure, meanwhile the method has some image enhancement ability.3) The informed preselection based nonlocal means filters are analyzed intensively, and pointed out that they all had defects in terms of feature extraction from image patch. We employ 2DPCA to extract feature from image patch and propose an efficient nonlocal means filter. Mainly, our contributions to the preselection based nonlocal means filter are: (l)patch-oriented 2DPCA for extracting features from image patches; (2)automatic selection of the similar sets based on the histogram of similarity distance.(3)adaptive determination of the similar weight coefficient parameter. Experimental results show that our method can achieve better filtering results in a variety of images, such as weak gradient image, face image and texture image.4) A structure tensor smoothing method based on the weighted curvature-preserving PDE is proposed. At first, informed anisotropic diffusion based structure tensor smoothing methods are analyzed and pointed out that the smoothing methods can not preserve 2D structure information in structure tensor data leading to that corresponding nonlinear structure tensor are deficient in extracting 2D structure information form image. Then, the image filtering method proposed in the third chapter of this thesis is extended to tensor field, and yield a weighted curvature-preserving PDE based tensor field filtering method. At last, structure tensor is smoothed by the new filtering method and produces a new nonlinear structure tensor. Experimental results indicate that new nonlinear structure tensor can be able to extract local 2D structure information from image well.5) A restoration framework for tilting mode satellite image based on the theory of theory of optimum design for image chain is proposed. At first, 2D sampling theorem and reciprocal cell theory are discussed especially, and employed to analyze the cause of aliasing in satellite image. Then, the distribution of the systemic modulation transfer function, noise and aliasing of acquisition system of tilting mode satellite image, is studied by using efficient resolution model and adaptive reciprocal cell, and we point out that there could be available malposed frequency spectrum in the tilting mode satellite image, in the context of undersampling. Based on this, a viewpoint that the malposed frequency spectrum can be acquired by increasing systemic cut-off frequency in tilting mode imaging system is proposed. At last, under such condition, we present a restoration framework consisting of four successive steps of generating adaptive reciprocal cell, extracting efficient frequency spectrum, upsampling and deblurring.6) Two deblurring methods are proposed for titling mode satellite image. At first, the data-fitting term of TV model is rewrite in the fourier domain and defined on the titling mode adaptive reciprocal cell, thus a adaptive reciprocal cell based TV regularization model(ARCTV) is constructed, which also employs the method presented in chapter1 in this thesis to compute the adaptive weight coefficients. Then, in order to improve the deblurring capacity of the ARCTV model, a gradient-fitting term is introduced into it and yields a modification. Future, the gradient-fitting is analyzed and pointed out that it has the ability of compensating high frequency information. Experimental results show that the two proposed methods can achieve good deblured results and the deblurred results by the modified ARCTV method are better, both in visual effect and SNR values.