Learning Implicit Neural Degradation Representation for Unpaired Image Dehazing

2025-11-17

AI Summary

Addressing the challenge of jointly modeling non-uniform haze distribution and preserving global consistency in complex scenes, this paper proposes an unsupervised image dehazing method. We introduce the Kolmogorov–Arnold representation theorem into visual degradation modeling for the first time, constructing a continuous haze concentration field via implicit neural representations (INRs), thereby eliminating reliance on explicit feature extraction or handcrafted physical priors. To enhance structural fidelity while suppressing redundancy, we further design a channel-decoupled learning mechanism and a densely connected residual enhancement module. The method operates without paired training data and achieves state-of-the-art performance across multiple public and real-world hazy datasets. Notably, it demonstrates superior robustness and reconstruction fidelity in challenging scenarios characterized by highly non-uniform haze distribution and complex illumination conditions.

Technology Category

• Computer Vision: Representation Learning for Vision
• Machine Learning: Representation Learning
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Image dehazing is an important task in the field of computer vision, aiming at restoring clear and detail-rich visual content from haze-affected images. However, when dealing with complex scenes, existing methods often struggle to strike a balance between fine-grained feature representation of inhomogeneous haze distribution and global consistency modeling. Furthermore, to better learn the common degenerate representation of haze in spatial variations, we propose an unsupervised dehaze method for implicit neural degradation representation. Firstly, inspired by the Kolmogorov-Arnold representation theorem, we propose a mechanism combining the channel-independent and channel-dependent mechanisms, which efficiently enhances the ability to learn from nonlinear dependencies. which in turn achieves good visual perception in complex scenes. Moreover, we design an implicit neural representation to model haze degradation as a continuous function to eliminate redundant information and the dependence on explicit feature extraction and physical models. To further learn the implicit representation of the haze features, we also designed a dense residual enhancement module from it to eliminate redundant information. This achieves high-quality image restoration. Experimental results show that our method achieves competitive dehaze performance on various public and real-world datasets. This project code will be available at https://github.com/Fan-pixel/NeDR-Dehaze.

Problem

• Modeling implicit neural degradation representation for unpaired image dehazing
• Balancing fine-grained haze feature representation with global consistency
• Eliminating dependency on explicit feature extraction and physical models

Innovation

• Implicit neural representation models haze degradation continuously
• Channel-independent and dependent mechanisms enhance nonlinear dependency learning
• Dense residual enhancement module eliminates redundant information for restoration