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In super-resolution (SR) reconstruction of images, regularization becomes crucial when insufficient number of measured low-resolution images is supplied. Beyond making the problem algebraically well posed, a properly chosen regularization can direct the solution toward a better quality outcome. Even the extreme case-a SR reconstruction from a single measured image-can be made successful with a well-chosen regularization. Much of the progress made in the past two decades on inverse problems indoi:10.1093/comjnl/bxm008 fatcat:4lhp4f3vfzcdnjh4jugyiklt7u