Super-resolution (SR) is a technique to obtain a higher resolution image (frame) by fusing multiple low-resolution (LR)
images (frames) of the same scene. In a typical super-resolution algorithm, image registration is one of the most affective
steps. The diffculty of this step results in the fact that most of the existing SR algorithms can not cope with local motions
because image registration in general assumes global motion. Moreover, modeling SR noise including image registration
error has great influence on the performance of the SR algorithms. In this paper, we report that Laplacian distribution
assumption is good selection for global and slow motions that can be easily registered, while for fast motion sequences
that contain multi-moving objects, Gaussian distribution is better for error modeling. Based on these results, we propose
a cost function with weighted L2-norm considering the SR noise model where the weights are generated from the error
of registration and penalize parts that are inaccurately registered. These weights serve to reject the outlier image regions.
Both the objective and subjective results demonstrate that the proposed algorithm gives better results for slow and fast
motion sequences.
We consider the problem of recovering a high-resolution (HR) frame from a sequence of low-resolution (LR) frames. It
is challenging to design a super-resolution (SR) algorithm for arbitrary video sequences. Video frames in general cannot
be related through global parametric transformation due to the arbitrary individual pixel movement between frame
pairs. Hence a local motion model needs to be used for frame alignment. An accurate alignment is the key to success
of reconstruction-based super-resolution algorithms. Motivated by this challenge we propose to employ region-matching
technique for image registration in this paper. The proposed algorithm consists of the alignment step to produce a blurred
version of the HR frame and the restoration step to estimate the HR frame. The experimental results of the proposed
algorithm are compared with the results of using affine, block matching, and optical flow motion models. It is shown that
the use of region matching for SR is very promising in producing higher quality images.
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