SEAL: A Framework for Systematic Evaluation of Real-World Super-Resolution

TL;DR

SEAL introduces a systematic evaluation framework for real-world super-resolution methods by clustering degradation types and proposing new metrics, enabling comprehensive benchmarking and insights into method performance.

Contribution

The paper presents a novel evaluation framework that clusters degradation types and introduces new metrics for systematic assessment of real-SR methods.

Findings

Existing methods evaluated on limited degradation cases

SEAL provides a comprehensive test set covering diverse degradations

New insights into the performance and robustness of real-SR methods

Abstract

Real-world Super-Resolution (Real-SR) methods focus on dealing with diverse real-world images and have attracted increasing attention in recent years. The key idea is to use a complex and high-order degradation model to mimic real-world degradations. Although they have achieved impressive results in various scenarios, they are faced with the obstacle of evaluation. Currently, these methods are only assessed by their average performance on a small set of degradation cases randomly selected from a large space, which fails to provide a comprehensive understanding of their overall performance and often yields inconsistent and potentially misleading results. To overcome the limitation in evaluation, we propose SEAL, a framework for systematic evaluation of real-SR. In particular, we cluster the extensive degradation space to create a set of representative degradation cases, which serves as a comprehensive test set. Next, we propose a coarse-to-fine evaluation protocol to measure the distributed and relative performance of real-SR methods on the test set. The protocol incorporates two new metrics: acceptance rate (AR) and relative performance ratio (RPR), derived from acceptance and excellence lines. Under SEAL, we benchmark existing real-SR methods, obtain new observations and insights into their performance, and develop a new strong baseline. We consider SEAL as the first step towards creating a comprehensive real-SR evaluation platform, which can promote the development of real-SR. The source code is available at https://github.com/XPixelGroup/SEAL