Joint Non-Gaussian Denoising and Superresolving of Raw High Frame Rate Videos

High frame rate cameras capture sharp videos of highly dynamic scenes by trading off signal-noise-ratio and image resolution, so combinational super-resolving and denoising is crucial for enhancing high speed videos and extending their applications. The solution is nontrivial due to the fact that two deteriorations co-occur during capturing and noise is nonlinearly dependent on signal strength. To handle this problem, we propose conducting noise separation and super resolution under a unified optimization framework, which models both spatiotemporal priors of high quality videos and signal-dependent noise. Mathematically, we align the frames along temporal axis and pursue the solution under the following three criterion: 1) the sharp noise-free image stack is low rank with some missing pixels denoting occlusions; 2) the noise follows a given nonlinear noise model; and 3) the recovered sharp image can be reconstructed well with sparse coefficients and an over complete dictionary learned from high quality natural images. In computation aspects, we propose to obtain the final result by solving a convex optimization using the modern local linearization techniques. In the experiments, we validate the proposed approach in both synthetic and real captured data.