Research On Image Resolution-Enhancement Based On Regularization Models With Sparsity Constraints

To acquire high resolution synthetic aperture radar (SAR) images and optical remote sensing images is one of the main development directions of remote reconnaissance and surveillance. But the high cost and the current hardware techniques limit the improvement of resolution of remote images. Therefore, it is of theoretical and practical significance to study relative data processing methods to improve image qualities and enhance the resolution.This thesis works on the background of ill-posed problems of resolution enhancement of SAR and optical images. The thesis has studied the effective application of sparsity constraints to resolution enhancement, and relative problems of sparsity constrained regularization. The main contributions of this thesis are listed as follows:1) The thesis systematically reveals the sparsity mechanism of target scattering centers in the SAR images from multiple perspectives.The causes of sparsity of strong scattering centers in the SAR imaging scene are investigated. Firstly, according to the conception of target scattering centers and the electromagnetic scattering theory, the theoretical and experimental analyses show the physical reason of the sparse property of scattering centers, which are brighter points in the image. Secondly, considering the mechanism of influence of backscattering intensity by the rough refraction surface of imaging area, the paper reveals the statistical property and physical reason the"near-black"characteristic of most scenes in SAR images. Finally, the thesis compares the different sparsity configurations of SAR and optical images, in view of the sparse decomposition properties of human visual system.2) The thesis exploits Lorentzian function to the domain of SAR image resolution enhancement.Based on the Cauchy distribution of the amplitude of SAR image, the thesis presents a regularization model with Lorentzian function as a sparsity constraint term. A fast algorithm is proposed to solve the regularization model. The robustness and the shrinkage property of the model are analyzed. At last, the paper concludes that the solution is general sparse, i.e. most data of the acquired solution are near-zero, few are far from zero. The experimental results with SAR images proof the above properties.3) The thesis presents a new sparsity constraint regularization model with variable l p quasi-norms with application to resolution enhancement. Generally, the current resolution enhancement regularization models take a fixed l p quasi-norm term as the regularization term, and keep it invariant in the model-solving process. This presents a new regularization model, whose l p quasi-norm term is not fixed and changes in accordance of the image data. We call it a regularization model with variable l p quasi-norms. According to the relation between l p quasi-norm term and the shape parameter of generalized Gaussian distributed data, the paper proposes a method to determine this regularization term. The regularization model leads to an alternating iterative algorithm to seek the optimal solution.4) The thesis deduces the relations of quasi-norm sparsity constraint regularization models and the sparsity property of solution, and presents a partial shrinkage algorithm.Taking different l p quasi-norm regularization term, the solution of regularization model will be of different degree of sparsity. The thesis studies the relations of sparse regularization term and the solution, and form the determination property of parameter p of the l p quasi-norm constraint regularization model. The thesis deduces the threshold of the model, and points out the relation of the threshold and l p quasi-norm regularization term and the regularization parameter, and proposes of partial shrinkage algorithm to protect the intensity of SAR images scattering centers. The relation between noise and the sparsity of solution is also deduced. The above works form a foundation of the determination mechanism of the sparsity of solution by the regularization model.5) The thesis studies the resolution enhancement methods of optical images under the non-Gaussian noise, presents a framework of regularization with non-quadratic data measurements and sparsity constraint terms.According to the Bayesian estimation and variation method, the thesis construct different regularization models with non-quadratic regularization terms with application to blurred optical images with different non-Gaussian noise, i.e. generalized Gaussian distributed noise and Poisson distributed noise. The sparsity of the edges is considered in the regularization model. The thesis discusses different algorithm for each regularization model. The thesis demonstrates the resolution enhancement abilities of different models with remote sensing images and common optical images, and the results show the effectiveness of the algorithms.