Improving Motion Deblur By Multi-Output Learning

Image deblurring is an ill-posed task, where exists infinite feasible solutions for blurry images. Modem deep learning approaches usually discard the learning of blur kernels and directly employ end-to-end supervised learning. However, supervised learning can’t handle ill-posed tasks appropriately. It regresses the average thus losing sharp details. Therefore, we propose an extension to the network to learn from stochastic supervisions, where a novel multi-output architecture and Min-Out loss function are designed. Our approach enables the model to output multiple feasible solutions to fit various non-uniform motions. We then propose a novel parameter multiplexing method that reduces computations while improving performance with fewer parameters. After training, the best-performed head is fine-tuned to be used for inference where the sharp label is absent. The proposed approach is evaluated with multiple image deblur models on the GoPro motion deblur dataset. On average, the multi-output extension improves the PSNR by 0.08 dB. When applied to the popular attention-based model Restormer, the multi-output helps it achieve 33.05 dB (+0.13 dB) PSNR without modification on network architecture.

1. INTRODUCTION

Image deblurring, aiming at finding a sharp image from a blurry one under an unknown degradation operation, is one of the fundamental problems in low-level computer vision. Motion deblurring is one of the deblurring tasks, where degradation is mainly caused by the motion of the camera or objects. The task of image deblurring is to remove the blurry trace and restore the sharp image.