From Zero to Detail: A Progressive Spectral Decoupling Paradigm for UHD Image Restoration with New Benchmark

2026-04-16

AI Summary

This work addresses the significant challenges in ultra-high-definition (UHD) image restoration, which arise from high resolution, content complexity, and intricate structural details. The authors propose a progressive spectral decoupling paradigm that decomposes the restoration process into three sequential stages: zero-frequency enhancement, low-frequency recovery, and high-frequency refinement, integrated within a unified ERR framework. Key innovations include a novel stage-wise spectral processing mechanism, a frequency-windowed Kolmogorov–Arnold Network (FW-KAN), and LSUHDIR—the first large-scale, high-quality benchmark dataset for UHD image restoration. Extensive experiments demonstrate state-of-the-art performance across multiple UHD restoration tasks, while ablation studies confirm the effectiveness of each component, substantially advancing the field.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Ultra-high-definition (UHD) image restoration poses unique challenges due to the high spatial resolution, diverse content, and fine-grained structures present in UHD images. To address these issues, we introduce a progressive spectral decomposition for the restoration process, decomposing it into three stages: zero-frequency \textbf{enhancement}, low-frequency \textbf{restoration}, and high-frequency \textbf{refinement}. Based on this formulation, we propose a novel framework, \textbf{ERR}, which integrates three cooperative sub-networks: the zero-frequency enhancer (ZFE), the low-frequency restorer (LFR), and the high-frequency refiner (HFR). The ZFE incorporates global priors to learn holistic mappings, the LFR reconstructs the main content by focusing on coarse-scale information, and the HFR adopts our proposed frequency-windowed Kolmogorov-Arnold Network (FW-KAN) to recover fine textures and intricate details for high-fidelity restoration. To further advance research in UHD image restoration, we also construct a large-scale, high-quality benchmark dataset, \textbf{LSUHDIR}, comprising 82{,}126 UHD images with diverse scenes and rich content. Our proposed methods demonstrate superior performance across a range of UHD image restoration tasks, and extensive ablation studies confirm the contribution and necessity of each module. Project page: https://github.com/NJU-PCALab/ERR.

Problem

• UHD image restoration
• high spatial resolution
• fine-grained structures
• diverse content

Innovation

• Progressive Spectral Decoupling
• Frequency-Windowed KAN
• UHD Image Restoration
• Zero-Frequency Enhancement
• LSUHDIR Benchmark