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Super-resolution reconstruction (SR) is a technique for estimating a high resolution (HR) image from multiple low resolution (LR) copies captured from the same scene. Most of the existing SR algorithms are based on the assumption that the scene moves parallel to the camera lens with translational or rotational motion. However, such an assumption may not be held if zooming exists when acquiring LR...
In learning based single image super-resolution (SR) approach, the super-resolved image are usually found or combined from training database through patch matching. But because the representation ability of small patch is limited, it is difficult to guarantee that the super-resolved image is best under global view. To tackle this problem, we propose a statistical learning method for SR with both global...
In this paper, we present an improved algorithm for super-resolution image restoration which used L-Curve regularization to Maximum A-Posteriori method. Super-Resolution as the second-generation problem of image restoration is also an ill-posed question because of an insufficient number of LR images and ill-conditioned blur operators. Procedures adopted to stabilize the inversion of ill-posed problem...
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