Robust Video SuperResolution with Registration Efficiency Adaptation SuperResolution (SR) is a technique to construct a highresolution (HR) frame by fusing a group of lowresolution (LR) frames describing the same scene. The effectiveness of the conventional superresolution 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 superresolution video. This paper proposes a robust video superresolution technique that adapts itself to the spatiallyvarying 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.

Superresolution based on regionmatching motion estimation We consider the problem of recovering a highresolution (HR) frame from a sequence of lowresolution (LR) frames. It is challenging to design a superresolution (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 reconstructionbased superresolution algorithms. Motivated by this challenge we propose to employ regionmatching 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.