Joint image deblurring and super-resolution with attention dual supervised network

Image deblurring and super-resolution (SR) are computer vision tasks aiming to restore image detail and spatial scale, respectively. Despite significant research effort over the past years, it remains challenging for joint image deblurring and SR via deep networks. Besides, only a few recent literatures contribute to this task, as conventional methods deal with SR or deblurring separately. To rectify the weakness, we propose a novel network that handles both tasks jointly and in this way boosts the SR performance from blurry input greatly. To fully exploit the representation capacity of our model, dual supervised learning is proposed to impose the constraint between low-resolution (LR) and high-resolution (HR) images. Our model consists of three parts: (i) a deblurring module equipped with channel attention residual blocks that removes the fuzziness from input images, (ii) an SR module to super-resolve the image based on the feature maps from the deblurring module serving as input, and (iii) a dual module which exploits the dependencies between LR and HR images. Extensive experiments indicate that the proposed attention dual supervised network (ADSN) not only generates remarkably clear HR images, but also achieves compelling results for joint image deblurring and SR task.