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Remote sensing image reconstruction algorithm oriented IICCD camera is not completely random sampling

Author: ShaoJun
Tutor: XiaoLiang
School: Nanjing University of Technology and Engineering
Course: Applied Computer Technology
Keywords: Not completely random sampling Sparsity Regularization Image Restoration IICCD camera
CLC: TP751
Type: Master's thesis
Year: 2011
Downloads: 61
Quote: 0
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Abstract


Since the The image enhanced CCD (Ⅱ CCD) camera with a high signal-to-noise ratio, high gain, and in the dim light under the conditions of stable work, etc., and has broad prospects for application in remote sensing and military. However, due to the inevitable Ⅱ CCD camera imaging optical blur, noise quality degradation process, as well as high-resolution image data transmission, so people want to study the reconstruction of incomplete sampling of remote sensing image restoration. Currently, combined with the sparse representation theory of image restoration and regularization method and algorithm research is an international research hotspot. This paper summarized the current compression perception and image restoration technology research status on the basis of variational regularization of image restoration technology as the main line, the model and algorithm of the restored version of the image from fully sampled and incomplete sampling both cases. The main innovation of this paper include: First, the coupled image restoration (TV-a) model based on total variation (TV) regularization and sparsity constraints. Model under the l1 sparsity and data fidelity model of joint optimization through the Total Variation images model, image Curvelet transform, the image edge structure and texture feature preserving image restoration. In this paper, the optimization model for solving the problem, based on the operator splitting method principle, design of a multi-step iterative numerical algorithm. Experiments show that the visual quality of the restored image of the proposed algorithm is better than TV restoration algorithm (FTVdG), recovery results quickly. Not completely random sampling remote sensing image restoration (deblurring), design and realization of the image restoration based on Curvelet shrinkage and Poisson singular integral reconstruction algorithm (Curvelet-PSI); propose a Curvelet iterative threshold contraction and Fourier-based contraction ( FoRD) image restoration and reconstruction algorithm (Curvelet FoRD),. This article the new algorithm the Curvelet-FoRD and the Curvelet-PSI algorithm has considerable performance recovery and reconstruction: a few parameters, parameter adjustment is simple, easy to quickly realize the advantages. Third, cross-eyes the IICCD camera system, a comprehensive analysis the IICCD camera imaging mechanism established by optical transfer function and the noise characteristics IICCD image degradation model; proposed completely sampled under the TV-l1 IICCD image restoration algorithm; design and give under the the Curvelet-FoRD not completely random sampling reconstruction algorithm for image restoration. Experiments to prove the effectiveness of the proposed algorithm.

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CLC: > Industrial Technology > Automation technology,computer technology > Remote sensing technology > Interpretation, identification and processing of remote sensing images > Image processing methods
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