Towards Optimal Aggregation of Varying Range Dependencies in Haze Removal

• URL: http://arxiv.org/abs/2408.12317v2
• Date: Mon, 10 Mar 2025 01:14:24 GMT
• Title: Towards Optimal Aggregation of Varying Range Dependencies in Haze Removal
• Authors: Xiaozhe Zhang, Fengying Xie, Haidong Ding, Linpeng Pan, Zhenwei Shi,
• Abstract summary: Haze removal aims to restore a clear image from a hazy input.<n>Existing methods have shown significant efficacy by capturing either short-range dependencies for local detail preservation or long-range dependencies for global context modeling.<n>We propose bfDehazeMatic, which captures both short- and long-range dependencies through dual-path design for improved restoration.
• Score: 17.29370328189668
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Haze removal aims to restore a clear image from a hazy input. Existing methods have shown significant efficacy by capturing either short-range dependencies for local detail preservation or long-range dependencies for global context modeling. Given the complementary strengths of both approaches, a intuitive advancement is to explicitly integrate them into a unified framework. However, this potential remains underexplored in current research. In this paper, we propose \textbf{DehazeMatic}, which leverages the proposed Transformer-Mamba Dual Aggregation block to simultaneously and explicitly captures both short- and long-range dependencies through dual-path design for improved restoration. To ensure that dependencies at varying ranges contribute optimally to performance, we conduct extensive experiments to identify key influencing factors and determine that an effective aggregation mechanism should be guided by the joint consideration of haze density and semantic information. Building on these insights, we introduce the CLIP-enhanced Dual-path Aggregator, which utilizes the rich semantic priors encapsulated in CLIP and the estimated haze density map, derived from its powerful generalization ability, to instruct the aggregation process. Extensive experiments demonstrate that DehazeMatic outperforms sort-of-the-art methods across various benchmarks.

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