Locally Adaptive Kernel Regression for Space-Time Super-Resolution
In this chapter, we discuss a novel framework for adaptive enhancement and spatiotemporal upscaling of videos containing complex motions. Our approach is based on multidimensional kernel regression, where each pixel in the video sequence is approximated with a 3-D local (Taylor) series, capturing the essential local behavior of its spatiotemporal neighborhood. The coefficients of this series are estimated by solving a local weighted least-squares problem, where the weights are a function of the 3-D space-time orientation in the neighborhood. As this framework is fundamentally based upon the comparison of neighboring pixels in both space and time, it implicitly 64contains information about the local motion of the pixels across time, therefore rendering unnecessary an explicit computation of motions of modest size. When large motions are present, a basic, rough motion compensation step returns the sequence to a form suitable again for motion-estimation-free super-resolution. The proposed approach not only significantly widens the applicability of super-resolution methods to a broad variety of video sequences containing complex motions, but also yields improved overall performance. Using several examples, we illustrate that the developed algorithm has super-resolution capabilities that provide improved optical resolution in the output, while being able to work on general input video with essentially arbitrary motion.