Concurrent Video Denoising and Deblurring for Dynamic Scenes

Abstract

Dynamic scene video deblurring is a challenging task due to the spatially variant blur inflicted by independently moving objects and camera shakes. Recent deep learning works bypass the ill-posedness of explicitly deriving the blur kernel by learning pixel-to-pixel mappings, which is commonly enhanced by larger region awareness. This is a difficult yet simplified scenario because noise is neglected when it is omnipresent in a wide spectrum of video processing applications. Despite its relevance, the problem of concurrent noise and dynamic blur has not yet been addressed in the deep learning literature. To this end, we analyze existing state-of-the-art deblurring methods and encounter their limitations in handling non-uniform blur under strong noise conditions. Thereafter, we propose a first-to-date work that addresses blur- and noise-free frame recovery by casting the restoration problem into a multi-task learning framework. Our contribution is threefold: a) We propose R2-D4, a multi-scale encoder architecture attached to two cascaded decoders performing the restoration task in two steps. b) We design multi-scale residual dense modules, bolstered by our modulated efficient channel attention, to enhance the encoder representations via augmenting deformable convolutions to capture longer-range and object-specific context that assists blur kernel estimation under strong noise. c) We perform extensive experiments and evaluate state-of-the-art approaches on a publicly available dataset under different noise levels. The proposed method performs favorably under all noise levels while retaining a reasonably low computational and memory footprint.