Existing methods attempt to improve models' generalization ability on real-world hazy images by exploring well-designed training schemes (e.g., cycleGAN, prior loss). However, most of them need very complicated training procedures to achieve satisfactory results. In this work, we present a totally novel testing pipeline called Prompt-based Test-Time Dehazing (PTTD) to help generate visually pleasing results of real-captured hazy images during the inference phase. We experimentally find that given a dehazing model trained on synthetic data, by fine-tuning the statistics (i.e., mean and standard deviation) of encoding features, PTTD is able to narrow the domain gap, boosting the performance of real image dehazing. Accordingly, we first apply a prompt generation module (PGM) to generate a visual prompt, which is the source of appropriate statistical perturbations for mean and standard deviation. And then, we employ the feature adaptation module (FAM) into the existing dehazing models for adjusting the original statistics with the guidance of the generated prompt. Note that, PTTD is model-agnostic and can be equipped with various state-of-the-art dehazing models trained on synthetic hazy-clean pairs. Extensive experimental results demonstrate that our PTTD is flexible meanwhile achieves superior performance against state-of-the-art dehazing methods in real-world scenarios.

翻译：现有方法试图通过探索精心设计的训练方案（如循环生成对抗网络、先验损失）来提高模型在真实世界雾霾图像上的泛化能力。然而，大多数方法需要极其复杂的训练过程才能获得令人满意的结果。在这项工作中，我们提出了一种全新的测试流程，称为基于提示的测试时去雾（PTTD），旨在推理阶段帮助生成视觉上令人满意的真实采集雾霾图像。我们通过实验发现，给定一个在合成数据上训练的去雾模型，通过微调编码特征的统计量（即均值和标准差），PTTD能够缩小域差距，提升真实图像去雾的性能。据此，我们首先应用提示生成模块（PGM）生成视觉提示，该提示是为均值和标准差提供适当统计扰动的来源。然后，我们将特征适应模块（FAM）集成到现有去雾模型中，以在生成提示的指导下调整原始统计量。需要注意的是，PTTD是模型无关的，可配备各种基于合成雾霾-清晰图像对训练的最新去雾模型。大量实验结果表明，我们的PTTD灵活且能在真实场景中取得优于最新去雾方法的性能。