Training Your Image Restoration Network Better with Random Weight Network as Optimization Function

The blooming progress made in deep learning-based image restoration has been largely attributed to the availability of high-quality, large-scale datasets and advanced network structures. However, optimization functions such as L1 and L2 are still de facto. In this study, we propose to investigate new optimization functions to improve image restoration performance. Our key insight is that random weight network can be acted as a constraint for training better image restoration networks''. However, not all random weight networks are suitable as constraints.