提出压缩与自适应框架，高效适配真实场景去雾
CoA: Towards Real Image Dehazing via Compression-and-Adaptation
- 先在合成数据压缩模型，再在真实数据双层自适应
- 无需额外约束，实现跨域稳定与模型无关灵活
- 适合资源受限下需快速部署的真实去雾场景
基于学习的图像去雾算法在合成数据上表现优异，但在真实图像去雾中仍面临算力限制和场景多样性挑战。为此，本文提出压缩与自适应（CoA）计算流程，从分治视角应对效率与适应性难题。首先在合成域进行模型压缩，构建紧凑的去雾参数空间以满足高效需求；随后引入真实域的双层自适应机制，通过学习过程聚合合成域的去雾能力，使模型对未知真实环境具有鲁棒性。该设计简洁，无额外约束，展现出域无关稳定性与模型无关灵活性，有效弥合合成与真实域间的模型鸿沟，提升实际应用价值。大量实验与分析验证了方法的优越性与有效性。代码已公开于 https://github.com/fyxnl/COA。
原文摘要 · Abstract (English)
Learning-based image dehazing algorithms have shown remarkable success in synthetic domains. However, real image dehazing is still in suspense due to computational resource constraints and the diversity of real-world scenes. Therefore, there is an urgent need for an algorithm that excels in both efficiency and adaptability to address real image dehazing effectively. This work proposes a Compression-and-Adaptation (CoA) computational flow to tackle these challenges from a divide-and-conquer perspective. First, model compression is performed in the synthetic domain to develop a compact dehazing parameter space, satisfying efficiency demands. Then, a bilevel adaptation in the real domain is introduced to be fearless in unknown real environments by aggregating the synthetic dehazing capabilities during the learning process. Leveraging a succinct design free from additional constraints, our CoA exhibits domain-irrelevant stability and model-agnostic flexibility, effectively bridging the model chasm between synthetic and real domains to further improve its practical utility. Extensive evaluations and analyses underscore the approach's superiority and effectiveness. The code is publicly available at https://github.com/fyxnl/COA.
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