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Super-resolution algorithm based on snr and combination of error-parameter analysis and searching method super-resolution algorithm based on snr and combination of error-parameter analysis and searching method.
Content Provider | CiteSeerX |
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Author | Sun, Guoxia Liu, Ju Yan, Hua |
Abstract | Generally, super-resolution image restoration approach is an ill-posed problem and its inversion is stabilized by regularization. It is important to set proper regularization parameter. Furthermore in many practical situations the blurring process is generally unknown or is known only to within a set of parameters, it is necessary to incorporate the blur identification into the restoration procedure. Assuming geometric warp matrixes are known, this paper presents a scheme in which regularization parameter is estimated by Signal-Noise Ratio and error-parameter analysis is combined with searching method to identify blur function during super-resolution restoration. Simulation shows that in the scheme the method of estimating regularization parameter can improve adaptive control power, make convergence of restoration algorithm better during super-resolution image restoration, and adaptively adjust this parameter when low-resolution images have different Signal-Noise Ratio; the combination of error-parameter analysis and searching method can effectively identify blur function, which reduce the cost of computation and accelerate searching speed. |
File Format | |
Access Restriction | Open |
Subject Keyword | Low-resolution Image Many Practical Situation Blur Identification Error-parameter Analysis Restoration Procedure Super-resolution Restoration Super-resolution Algorithm Signal-noise Ratio Proper Regularization Parameter Adaptive Control Power Searching Method Super-resolution Algorithm Different Signal-noise Ratio Blur Function Regularization Parameter Super-resolution Image Restoration Approach Searching Method Geometric Warp Matrix Blurring Process Ill-posed Problem Restoration Algorithm Super-resolution Image Restoration |
Content Type | Text |
Resource Type | Article |