TASK-ADAPTIVE FEATURE MATCHING LOSS FOR IMAGE DEBLURRING

Image deblurring is a highly challenging and ill-posed image restoration problem. Contemporary deep learning-based approaches usually tackle this problem by exploiting the encoder-decoder-based models trained by the commonly used mean squared error loss with the feature matching loss as a regularization to obtain perceptual consistent restored results as the ground truths. We argue that since the general backbone models for computing feature matching loss are usually not trained on the image deblurring task, the loss lacks specific knowledge of blur and usually leads to suboptimal performance. To address this issue, we propose a task-adaptive feature matching loss for image deblurring where we synthesize blurred images in different blur extents and employ triplet loss to finetune the backbone model for learning specific blur priors. Then, we leverage the finetuned backbone to compute feature matching loss which can greatly enhance the existing image deblurring models for better perceptual results. With extensive experiments on the GoPro and RealBlur datasets, both qualitative and quantitative results show that the SOTA deblurring models trained with the proposed loss can effectively obtain better and sharper restored images in terms of various perceptual image quality metrics than the original models while maintaining comparable PSNR and SSIM performances.