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

2026-03-05

AI Summary

This study systematically evaluates the capabilities and limitations of generative image restoration (GIR) methods in practical applications, revealing a shift in failure modes from under-generation to over-generation. To this end, we establish a multidimensional evaluation framework that comprehensively analyzes the performance of diffusion models, GANs, PSNR-oriented approaches, and general-purpose generative models across key dimensions including detail fidelity, sharpness, semantic correctness, and overall perceptual quality. Through large-scale subjective and objective experiments, we identify the central challenges as balancing fine-grained detail preservation with semantic controllability. Leveraging these insights, we develop a novel image quality assessment (IQA) model better aligned with human perception, offering a new benchmark and guiding direction for future GIR research.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Natural Language Processing: Generation
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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.

Problem

• Generative Image Restoration
• perceptual realism
• detail quality
• semantic correctness
• over-generation

Innovation

• Generative Image Restoration
• multi-dimensional evaluation
• failure mode evolution
• semantic control
• perceptual IQA