Degradation-Aware Prompt Learning with Cross-Modal Compensation for Adverse Weather Removal

• URL: http://arxiv.org/abs/2608.06939v2
• Date: Mon, 10 Aug 2026 02:35:36 GMT
• Title: Degradation-Aware Prompt Learning with Cross-Modal Compensation for Adverse Weather Removal
• Abstract summary: Degradation-Aware Cross-Modal Prompt Compensation Network (DCMPC-Net)<n>DCMPC-Net consists of the Cross-Modal Prompt Generator (CMPG), Prompt-Guided Attention Alignment Module (PGAAM), and Dual Feature Compensation Module (DFCM)<n> experiments show that DCMPC-Net outperforms state-of-the-art methods in both task-specific and unified settings.
• Score: 56.34780145346335
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adverse weather causes diverse and complex image degradations, severely compromising the reliability of computer vision systems. Existing all-in-one restoration models attempt to address multiple degradation types within a unified framework, but often lack explicit spatial and semantic modeling of degradation characteristics, limiting their adaptability to diverse weather conditions. To address this limitation, we propose a Degradation-Aware Cross-Modal Prompt Compensation Network (DCMPC-Net) that leverages cross-modal degradation cues from a pretrained vision-language model to condition restoration features within a unified backbone. Specifically, our DCMPC-Net mainly consists of the Cross-Modal Prompt Generator (CMPG), Prompt-Guided Attention Alignment Module (PGAAM), and Dual Feature Compensation Module (DFCM). The CMPG integrates textual embeddings with visual features to produce degradation-aware prompts that encode degradation-related semantic and contextual cues. These prompts are injected into the decoder via a PGAAM, which adaptively aligns semantic information with degraded regions to facilitate context-aware restoration. To further enhance structural fidelity, DFCM is introduced that disentangles degradation artifacts from scene structures, thereby improving the reconstruction of fine textures and detailed content. By integrating cross-modal semantic guidance with spatial alignment and structural enhancement, DCMPC-Net achieves robust and perceptually consistent restoration across diverse weather conditions. Extensive experiments show that DCMPC-Net outperforms state-of-the-art methods in both task-specific and unified settings, achieving superior accuracy and visual fidelity.

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