Transcription of Deep Unfolding Network for Image Super-Resolution
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Deep Unfolding Network for Image Super-Resolution
Deep Unfolding Network for Image Super-Resolution Kai Zhang Luc Van Gool Radu Timofte Computer Vision Lab, ETH Zurich, Switzerland { , vangool, Abstract Learning-based single Image Super-Resolution (SISR) Degradation Process y = (x k) s +n methods are continuously showing superior effective- ness and efficiency over traditional model-based methods, SISR Process largely due to the end-to-end training. However, different x = f (y; s, k, ). from model-based methods that can handle the SISR prob- (A single model?). lem with different scale factors, blur kernels and noise lev- els under a unified MAP (maximum a posteriori) frame- work, learning-based methods generally lack such flexibil- Figure 1.}
ferent scale factors via a single end-to-end trained model (see Fig. 1). In this paper, we propose a deep unfolding super-resolution network (USRNet) to bridge the gap between learning-based methods and model-based methods. On one hand, similar to model-based methods, USRNet can effec-tively handle the classical degradation model (i.e., Eq. (1))
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