Dive into Coarse-to-Fine Strategy in Single Image Deblurring

The coarse-to-fine approach has gained significant popularity in the design of networks for single image deblurring. Traditional methods used to employ U-shaped networks with a single encoder and decoder, which may not adequately capture complex motion blur patterns. Inspired by the concept of multi-task learning, we dive into the coarse-to-fine strategy and propose an all-direction, multi-input and multi-output network for image deblurring (ADMMDeblur). ADMMDeblur has two distinct features. Firstly, it employs four decoders, each generating a unique residual representing a specific motion direction. This enables the network to effectively address motion blur in all directions within a two-dimensional (2D) scene. Secondly, the decoders utilize kernel rotation and sharing, which ensures the decoders do not separate unnecessary components. Consequently, the network exhibits enhanced efficiency and deblurring performance while requiring fewer parameters. Extensive experiments conducted on the GoPro and HIDE datasets demonstrate that our proposed network achieves better performance in deblurring accuracy and model size compared to existing well-performing methods.