Enhanced Image Restoration Via Supervised Target Feature Transfer

Deep learning has obtained remarkable success for image restoration. However, most existing deep image restoration models are trained by minimizing the pixel-level reconstruction error between restored images and target images (ground truth), while neglecting the rich information from the intermediate feature layers, thus hindering the representational power of networks. To address this problem, we propose a Supervised Target Feature Transfer (STFT) framework to enhance the power of feature expression of the deep image restoration models. Specifically, we introduce a self-supervised antoencoder-based target feature extractor to extract compact feature representation of target images, which serves as supervision signals to train the deep backbone models at the same time. With such feature-level supervised information, deep backbone model can be enhanced by transfer learning of such target features. Moreover, we theoretically analyze our STFT training strategies and demonstrate that it imposes learnable prior information on the backbone restoration model. Extensive experiments demonstrate the effectiveness of our proposed framework compared with the state-of-the-art image restoration models.