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

Area of Science:

• Computer Vision
• Image Processing
• Deep Learning

Background:

• Ultra-high-definition (UHD) image restoration presents significant challenges due to high resolution and complex details.
• Existing methods often struggle with the intricate structures and diverse content inherent in UHD imagery.

Purpose of the Study:

• To develop an advanced framework for high-fidelity UHD image restoration.
• To address the limitations of current restoration techniques for high-resolution images.

Main Methods:

• A progressive spectral decomposition approach dividing restoration into zero-frequency enhancement, low-frequency restoration, and high-frequency refinement.
• A novel framework, ERR, integrating three cooperative sub-networks: ZFE, LFR, and HFR.
• Introduction of a frequency-windowed Kolmogorov-Arnold Network (FW-KAN) for fine texture recovery.

Main Results:

• The proposed ERR framework demonstrates superior performance in various UHD image restoration tasks.
• Ablation studies validate the effectiveness and necessity of each module within the ERR framework.
• The framework successfully recovers fine textures and intricate details, achieving high-fidelity results.

Conclusions:

• The progressive spectral decomposition and ERR framework offer a significant advancement in UHD image restoration.
• The developed FW-KAN module is crucial for detailed refinement in high-frequency components.
• The introduction of the LSUHDIR dataset facilitates further research in this domain.