Prompt-based test-time real image dehazing: a novel pipeline

• URL: http://arxiv.org/abs/2309.17389v5
• Date: Sun, 27 Oct 2024 04:33:39 GMT
• Title: Prompt-based test-time real image dehazing: a novel pipeline
• Authors: Zixuan Chen, Zewei He, Ziqian Lu, Xuecheng Sun, Zhe-Ming Lu,
• Abstract summary: We present Prompt-based Test-Time Dehazing (PTTD) to help generate visually pleasing results of real-captured hazy images. We experimentally observe that given a dehazing model trained on synthetic data, fine-tuning the statistics (ie, mean and standard deviation) of encoding features is able to narrow the domain gap. PTTD is model-agnostic and can be equipped with various state-of-the-art dehazing models trained on synthetic hazy-clean pairs to tackle the real image dehazing task.
• Score: 9.229160145438469
• License:
• Abstract: Existing methods attempt to improve models' generalization ability on real-world hazy images by exploring well-designed training schemes (\eg, CycleGAN, prior loss). However, most of them need very complicated training procedures to achieve satisfactory results. For the first time, we present a novel 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 observe that given a dehazing model trained on synthetic data, fine-tuning the statistics (\ie, mean and standard deviation) of encoding features 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 reference of appropriate statistical perturbations for mean and standard deviation. Then, we employ a feature adaptation module (FAM) into the existing dehazing models for adjusting the original statistics with the guidance of the generated prompt. PTTD is model-agnostic and can be equipped with various state-of-the-art dehazing models trained on synthetic hazy-clean pairs to tackle the real image dehazing task. Extensive experimental results demonstrate that our PTTD is effective, achieving superior performance against state-of-the-art dehazing methods in real-world scenarios. The code is available at \url{https://github.com/cecret3350/PTTD-Dehazing}.

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