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Task-Aligned Haze Removal With Semantic-Aware Fusion and Contrast Self-Correction
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DOI：10.1049/cit2.70162.png)
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Abstract
En 中文
Adverse haze conditions introduce complex degradations that obscure scene details and distort structural cues critical for object detection, posing persistent challenges for vision-based sensing systems. Although existing haze removal methods have achieved notable improvements in visual clarity, their optimisation objectives are often misaligned with downstream detection requirements, leading to limited detection performance in real-world scenarios. To address this issue, this work proposes a task-aligned weakly supervised haze removal framework, termed Dehaze4Detection, which explicitly aligns low-level restoration with high-level detection objectives. The framework incorporates a Semantic-Aware Multi-Scale Fusion Module (SMFM) that embeds pixel-level semantic knowledge into the dehazing process, enabling selective enhancement of object regions while suppressing background over-enhancement to achieve detection-friendly restoration. To further improve feature quality, a Contrast Self-Correction Attention Module (CSAM) is introduced, which exploits contrast maps derived from pseudo-dehazed images to guide attention towards object contours and salient structures. The proposed SMFM and CSAM are integrated into a unified pipeline and jointly optimised using detection and dehazing losses. Extensive experiments on both synthetic and real-world benchmarks demonstrate that Dehaze4Detection achieves competitive perceptual quality while significantly improving detection accuracy.
Keywords:
contrast self-correction
haze removal
low-level vision
semantic fusion
task-aligned
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