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. The source code of our PTTD will be made available at
https://github.com/cecret3350/PTTD-Dehazing.Comment: update github link (https://github.com/cecret3350/PTTD-Dehazing