Super-Resolution (SR) is a technique to construct a high-resolution (HR) frame by fusing a group of low-resolution (LR)
frames describing the same scene. The effectiveness of the conventional super-resolution techniques, when applied on
video sequences, strongly relies on the efficiency of motion alignment achieved by image registration. Unfortunately,
such efficiency is limited by the motion complexity in the video and the capability of adopted motion model. In image
regions with severe registration errors, annoying artifacts usually appear in the produced super-resolution video. This
paper proposes a robust video super-resolution technique that adapts itself to the spatially-varying registration efficiency.
The reliability of each reference pixel is measured by the corresponding registration error and incorporated into the
optimization objective function of SR reconstruction. This makes the SR reconstruction highly immune to the
registration errors, as outliers with higher registration errors are assigned lower weights in the objective function. In
particular, we carefully design a mechanism to assign weights according to registration errors. The proposed superresolution
scheme has been tested with various video sequences and experimental results clearly demonstrate the
effectiveness of the proposed method.
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