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A Nuclear-norm Model for Multi-Frame Super-resolution Reconstruction

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VMVW02 - Generative models, parameter learning and sparsity

In this talk, we give a new variational approach to obtain super-resolution images from multiple low-resolution image frames extracted from video clips. First the displacement between the low-resolution frames and the reference frame are computed by an optical flow algorithm. The displacement matrix is then decomposed into product of two matrices corresponding to the integer and fractional displacement matrices respectively. The integer displacement matrices give rise to a non-convex low-rank prior which is then convexified to give the nuclear-norm regularization term. By adding a standard 2-norm data fidelity term to it, we obtain our proposed nuclear-norm model. Alternating direction method of multipliers can then be used to solve the model. Comparison of our method with other models on synthetic and real video clips shows that our resulting images are more accurate with less artifacts. It also provides much finer and discernable details. Joint work with Rui Zhao. Research supported by HKRGC .

This talk is part of the Isaac Newton Institute Seminar Series series.

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