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Research On Infrared Image Super-resolution Reconstruction

Posted on:2016-04-17Degree:MasterType:Thesis
Country:ChinaCandidate:Y HeFull Text:PDF
Abstract/Summary:PDF Full Text Request
The resolution of infrared imaging system is limited by the infrared detector. The imaging system of infrared imaging system has not produced a fundamental change. Because of the limitation of the hardware, it is difficult to improve the image resolution on the hardware, improving the resolution of the infrared image by using the technology of software is a good choice. This paper mainly from the single frame low resolution infrared image input and a sequence of infrared images of low resolution input two aspects to proceed, using single frame of the input image super resolution image reconstruction based on dictionary learning method, projection onto convex sets(POCS) algorithm is used to input image sequence super resolution image reconstruction. The two algorithms are evaluated by the peak signal to noise ratio, the structure self-similarity and the visual observation. The above two algorithms are described.The method based on dictionary learning is to analyze the image of a set of training samples and get the relation between the high and low resolution images. Then, the low resolution image is reconstructed by using the rule. In the dictionary learning algorithm of the high and low resolution dictionary construction, because of its huge data volume, the use of principal component analysis(PCA) for construction the sub matrix method and K-SVD algorithm to reduce the computational complexity. In the process of sparse representation of the image, the OMP algorithm is used to reduce the computational complexity. And according to the residual information of the reconstructed image, the reconstructed image quality is improved by using the modified iterative back projection method.Super resolution reconstruction of the set of low resolution infrared images input using POCS algorithm. The traditional POCS algorithm is improved and optimized in the process of image reference frame correction. By using edge detection operator, the image is divided into smooth region and non-smooth region. The image is modified by different modified PSF weight coefficients. Taking into account that the POCS algorithm is difficult to deal with the noise, the K-SVD dictionary learning method is introduced to improve the quality of high resolution infrared image reconstruction.
Keywords/Search Tags:Infrared image, Dictionary learning, OMP algorithm, Iterative back projection method, projection onto convex sets
PDF Full Text Request
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