SUMMARYA method for spatio-temporal resolution enhancement of video sequences based on super-resolution reconstruction is proposed. A new observation model is defined for accurate resolution enhancement, which enables subpixel motion in intermediate frames to be obtained. A modified optimization formula for obtaining a high-resolution sequence is also adopted.

A super-resolution method for simultaneously realizing resolution enhancement and motion blur removal based on adaptive prior settings are presented in this article. In order to obtain high-resolution (HR) video sequences from motion-blurred low-resolution video sequences, both of the resolution enhancement and the motion blur removal have to be performed. However, if one is performed after the other, errors in the first process may cause performance deterioration of the subsequent process. Therefore, in the proposed method, a new problem, which simultaneously performs the resolution enhancement and the motion blur removal, is derived. Specifically, a maximum a posterior estimation problem which estimates original HR frames with motion blur kernels is introduced into our method. Furthermore, in order to obtain the posterior probability based on Bayes' rule, a prior probability of the original HR frame, whose distribution 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 the ringing artifacts in smooth regions are realized. Consequently, based on these novel approaches, the proposed method can perform successful reconstruction of the HR frames. Experimental results show impressive improvements of the proposed method over previously reported methods.

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