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Based on Genetic Algorithm super- resolution image reconstruction study

Author: MaCheng
Tutor: MaLiYong
School: Harbin Institute of Technology
Course: Control Science and Engineering
Keywords: Image Restoration Super-resolution Catastrophic Genetic Algorithm Population Diversity Regularization
CLC: TP391.41
Type: Master's thesis
Year: 2008
Downloads: 85
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Abstract


The merits of image quality and a variety of computer vision on human visual system is very important , so the image recovery digital image processing has been an important research content. As a branch of image restoration , super- resolution image reconstruction problems are more and more people 's attention. In video surveillance, satellite imaging and medical diagnostic applications , due to physical constraints, people get the image resolution is not high enough to meet the actual needs. Super- resolution image reconstruction technique is the use of these low-resolution image sequence of redundant information between the frame images , reconstructed high-resolution image . This article consists of the following: firstly introduced super-resolution image reconstruction technology development , analysis of the technology enables the mathematical and physical basis and ill - posed on the common super-resolution reconstruction methods are outlined and discussed the super-resolution rate image reconstruction algorithm subjective and objective evaluation criteria. It also describes the operation of the genetic algorithm process , the basic operation and characteristics and so on. Followed by analysis of the super-resolution image reconstruction algorithm observation model , and discusses the geometric distortion , blurring, downsampling and noise model parameters . On this basis , for the simple genetic algorithm premature convergence problem , we propose a genetic algorithm based on high-resolution image catastrophic optimal estimation method . In this method, the fitness value of the population variance is catastrophic judgment condition , the use of conjugate gradient algorithm as the cataclysm operator to rebuild populations , thereby improving the diversity of population , to avoid falling into local optimal solution. While the scale catastrophic disaster conditions and analyzed according to the evolutionary generation and gives disaster condition adjustment method . Concludes with a discussion of the regularization parameter selection problem, adaptive regularization parameter of the basic nature and form were introduced. Combining local image information , built with space for self- adaptive regularization term . The regularization term added to the objective function of genetic algorithm , and applied to the super-resolution image reconstruction, the restoration result is further improved.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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