Enhancing Image Restoration through Learning Context-Rich and Detail-Accurate Features

2025-04-14

AI Summary

Image restoration faces two key challenges: (1) imbalance between spatial detail preservation and contextual semantic modeling, and (2) insufficient exploitation of frequency-domain information. To address these, we propose LCDNet, a multi-scale spatial-frequency collaborative modeling framework. Its core innovations are: (1) a Hybrid-Scale Frequency Selection Block (HSFSBlock) that adaptively selects beneficial frequency components from DCT/FFT domains; and (2) a Skip-Connection Attention Mechanism (SCAM) enabling dynamic fusion of multi-scale spatial and frequency features with noise-aware filtering. LCDNet integrates multi-scale convolution, residual learning, and end-to-end optimization. Extensive experiments demonstrate state-of-the-art (SOTA) or SOTA-comparable performance on deblurring, denoising, and super-resolution tasks. Notably, it achieves significant improvements in texture fidelity and structural consistency, validating the effectiveness of synergistic spatial-frequency modeling.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Image restoration involves recovering high-quality images from their corrupted versions, requiring a nuanced balance between spatial details and contextual information. While certain methods address this balance, they predominantly emphasize spatial aspects, neglecting frequency variation comprehension. In this paper, we present a multi-scale design that optimally balances these competing objectives, seamlessly integrating spatial and frequency domain knowledge to selectively recover the most informative information. Specifically, we develop a hybrid scale frequency selection block (HSFSBlock), which not only captures multi-scale information from the spatial domain, but also selects the most informative components for image restoration in the frequency domain. Furthermore, to mitigate the inherent noise introduced by skip connections employing only addition or concatenation, we introduce a skip connection attention mechanism (SCAM) to selectively determines the information that should propagate through skip connections. The resulting tightly interlinked architecture, named as LCDNet. Extensive experiments conducted across diverse image restoration tasks showcase that our model attains performance levels that are either superior or comparable to those of state-of-the-art algorithms.

Problem

• Balancing spatial details and contextual information in image restoration
• Addressing frequency variation comprehension in restoration methods
• Mitigating noise from skip connections in deep architectures

Innovation

• Multi-scale design balances spatial and frequency domains
• HSFSBlock captures and selects informative components
• SCAM optimizes skip connections with attention