The Best of Both Worlds: a Framework for Combining Degradation Prediction with High Performance Super-Resolution Networks
This work addresses the challenge of improving blind super-resolution for image processing applications by integrating two existing paradigms, offering an incremental but practical solution.
The authors tackled the problem of blind super-resolution by proposing a framework that combines degradation prediction with high-performance super-resolution networks, resulting in hybrid models that consistently achieve stronger SR performance than both non-blind and blind counterparts, as demonstrated through comprehensive testing.
To date, the best-performing blind super-resolution (SR) techniques follow one of two paradigms: A) generate and train a standard SR network on synthetic low-resolution - high-resolution (LR - HR) pairs or B) attempt to predict the degradations an LR image has suffered and use these to inform a customised SR network. Despite significant progress, subscribers to the former miss out on useful degradation information that could be used to improve the SR process. On the other hand, followers of the latter rely on weaker SR networks, which are significantly outperformed by the latest architectural advancements. In this work, we present a framework for combining any blind SR prediction mechanism with any deep SR network, using a metadata insertion block to insert prediction vectors into SR network feature maps. Through comprehensive testing, we prove that state-of-the-art contrastive and iterative prediction schemes can be successfully combined with high-performance SR networks such as RCAN and HAN within our framework. We show that our hybrid models consistently achieve stronger SR performance than both their non-blind and blind counterparts. Furthermore, we demonstrate our framework's robustness by predicting degradations and super-resolving images from a complex pipeline of blurring, noise and compression.