Reproduce “Real-World Single Image Super-Resolution: A Brief Review” on OpenResearch

About this paper

Honggang Chen, Xiaohai He, Linbo Qing, Yuanyuan Wu, Chao Ren, Ce Zhu

This review consolidates research in Real-World Single Image Super-Resolution (RSISR), addressing the performance degradation of SR models on real images due to complex degradation processes, and proposes a four-category taxonomy of existing methods. The paper provides an empirical comparison of various techniques and identifies critical challenges such as data scarcity and evaluation metric limitations.

Key insights

• The "domain gap" between synthetic training data and real-world low-resolution images, caused by complex and unknown degradation processes, is the central challenge hindering practical Single Image Super-Resolution (SISR) applications.
• Real-World Single Image Super-Resolution (RSISR) methods can be categorized into four distinct paradigms: degradation modeling-based, image pairs-based, domain translation-based, and self-learning-based, each addressing the domain gap differently.
• Key challenges in RSISR research include the scarcity of diverse real-world LR-HR datasets, the need for more robust and efficient algorithms capable of handling arbitrary degradations, and the development of reliable evaluation metrics that align with human perception.

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