Kernel-aware Raw Burst Blind Super-Resolution

Burst super-resolution (SR) provides a possibility of restoring rich details from low-quality images. However, since low-resolution (LR) images in practical applications have multiple complicated and unknown degradations, existing non-blind (e.g., bicubic) designed networks usually lead to a severe performance drop in recovering high-resolution (HR) images. Moreover, handling multiple misaligned noisy raw inputs is also challenging. In this paper, we address the problem of reconstructing HR images from raw burst sequences acquired from modern handheld devices. The central idea is a kernel-guided strategy which can solve the burst SR with two steps: kernel modeling and HR restoring. The former estimates burst kernels from raw inputs, while the latter predicts the super-resolved image based on the estimated kernels. Furthermore, we introduce a kernel-aware deformable alignment module which can effectively align the raw images with consideration of the blurry priors. Extensive experiments on synthetic and real-world datasets demonstrate that the proposed method can perform favorable state-of-the-art performance in the burst SR problem.