Publication Date:
2013-02-23
Description:
A super-resolution method for simultaneously realizing resolutionenhancement and motion blur removal based on adaptive prior settingsare presented in this article. In order to obtain high-resolution(HR) video sequences from motion-blurred low-resolution videosequences, both of the resolution enhancement and the motion blurremoval have to be performed. However, if one is performed after theother, errors in the first process may cause performancedeterioration of the subsequent process. Therefore, in the proposedmethod, a new problem, which simultaneously performs the resolutionenhancement and the motion blur removal, is derived. Specifically, amaximum a posterior estimation problem which estimates original HRframes with motion blur kernels is introduced into our method.Furthermore, in order to obtain the posterior probability based onBayes' rule, a prior probability of the original HR frame, whosedistribution can adaptively be set for each area, is newly defined.By adaptively setting the distribution of the prior probability,preservation of the sharpness in edge regions and suppression of theringing artifacts in smooth regions are realized. Consequently,based on these novel approaches, the proposed method can performsuccessful reconstruction of the HR frames. Experimental resultsshow impressive improvements of the proposed method over previouslyreported methods.
Topics:
Electrical Engineering, Measurement and Control Technology
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