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arXiv · 2603.05010

How far have we gone in Generative Image Restoration? A study on its capability, limitations and evaluation practices

Xiang Yin·Jinfan Hu·Zhiyuan You·Kainan Yan·Yu Tang·Chao Dong·Jinjin Gu
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Abstract

Generative Image Restoration (GIR) has achieved impressive perceptual realism, but how far have its practical capabilities truly advanced compared with previous methods? To answer this, we present a large-scale study grounded in a new multi-dimensional evaluation pipeline that assesses models on detail, sharpness, semantic correctness, and overall quality. Our analysis covers diverse architectures, including diffusion-based, GAN-based, PSNR-oriented, and general-purpose generation models, revealing critical performance disparities. Furthermore, our analysis uncovers a key evolution in failure modes that signifies a paradigm shift for the perception-oriented low-level vision field. The central challenge is evolving from the previous problem of detail scarcity (under-generation) to the new frontier of detail quality and semantic control (preventing over-generation). We also leverage our benchmark to train a new IQA model that better aligns with human perceptual judgments. Ultimately, this work provides a systematic study of modern generative image restoration models, offering crucial insights that redefine our understanding of their true state and chart a course for future development.

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Xiang Yin, Jinfan Hu, Zhiyuan You, Kainan Yan, Yu Tang, Chao Dong, Jinjin Gu. 2026-03-05. How far have we gone in Generative Image Restoration? A study on its capability, limitations and evaluation practices. https://arxiv.org/abs/2603.05010

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