Super-resolution Reconstruction Of Spaceborne Sar Images Using Sparsity Theory

Though high resolution radar satellites have an advantage in fields like emergency relief, the actual radar images obtained have low spatial resolution and a poor quality due to the factors such as the restrictions of hardware systems, non-ideal movement of platforms, poor imaging conditions, systematic coherent noise, etc. Therefore, it is of great importance to do the research on the super-resolution reconstruction of radar images to improve their interpretability with the methods of image processing. The research status shows that the super-resolution reconstruction of multichannel space-borne radar images is a hotspot in the international scientific research. This article discuss the super-resolution reconstruction of space-borne radar images from the angle of sparse and compression perception. The main contents are as follows:(1) Research on the degradation mechanism and the sparse priori characteristics of the space-borne radar image is carried out, and the theory and method of sparse decomposition is studied as well. This paper mainly discusses the fuzzy degraded factors of radar imaging system, as well as the expression of priori knowledge, and an idea of simulating the SAR IRF (Impulse Response Function) with the ellipsoid parabolic model is put forward. The radar coherent speckle noise and the expression of priori knowledge are studied, and a method of parallelizing the speckle suppression and reconstruction is proposed. The target scattering characteristics and the expression of priori knowledge are studied. The sparse characteristics of the space-borne SAR target scattering scene are discussed, including the sparseness of the image itself and parameters sparseness of the image parametric model, and the relationship of the scene roughness, backscattering characteristics and sparseness is studied from the perspective of the sparseness of the target scattering scene and visual sparse decomposition.(2) The regularized functional model of reconstruction of super resolution radar images from single path space-borne satellites is discussed respectively from deterministic variation model and Bayesian theory. The sparse transcendental constrains and parametric estimation of this model is studied and the result of experiments is applicability.(3) Different factors of multi-channel image sequences with the same sensor and look angle are analyzed in this paper, mainly including the movement differences between image sequences, degraded differences by blurring, noisy differences, geometric distortion differences, scale differences, etc. Image registration and rectification could be used to improve the movement differences and geometric distortion differences. It is the estimated elliptic parabolic model that be used to mend the degraded differences by blurring, where the noisy differences are improved by regularization noise constraint. Based on the single channel reconstruction model, the function model of super-resolution reconstruction of multi-channel space-borne radar images is proposed whose function and the parameter estimation could be evaluated by the prior knowledge achievements and analysis different factors of image sequences.(4) Study the optimization of algorithm and present the method of"convert matrix through multidimensional convolution"which speeds up image processing effectively.