LocalAdaptive and OutlierTolerant Image Alignment Using RBF Approximation Abstract   Image alignment is a crucial step to generate a high quality panorama. The stateoftheart approaches use localadaptive transformations to deal with multiview parallax, but still suffer from unreliable feature correspondences and high computational cost. In this paper, we propose a localadaptive and outliertolerant image alignment method using RBF (radial basis function) approximation. To eliminate the visible artifacts, the input images are warped according to a constructed projection error function, whose parameters are estimated by solving a linear system. The outliers are efficiently removed by screening out the abnormal weights of RBFs, such that better alignment quality can be achieved compared to the existing approaches. Moreover, a weight assignment strategy is introduced to further address the overfitting issues caused by extrapolation, and hence the global projectivity can be well preserved. The proposed method is computationally efficient, whose performance is verified by comparative experiments on several challenging cases.

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.