  This work presents an effective depth-consistency self-prompt Transformer for
image dehazing. It is motivated by an observation that the estimated depths of
an image with haze residuals and its clear counterpart vary. Enforcing the
depth consistency of dehazed images with clear ones, therefore, is essential
for dehazing. For this purpose, we develop a prompt based on the features of
depth differences between the hazy input images and corresponding clear
counterparts that can guide dehazing models for better restoration.
Specifically, we first apply deep features extracted from the input images to
the depth difference features for generating the prompt that contains the haze
residual information in the input. Then we propose a prompt embedding module
that is designed to perceive the haze residuals, by linearly adding the prompt
to the deep features. Further, we develop an effective prompt attention module
to pay more attention to haze residuals for better removal. By incorporating
the prompt, prompt embedding, and prompt attention into an encoder-decoder
network based on VQGAN, we can achieve better perception quality. As the depths
of clear images are not available at inference, and the dehazed images with
one-time feed-forward execution may still contain a portion of haze residuals,
we propose a new continuous self-prompt inference that can iteratively correct
the dehazing model towards better haze-free image generation. Extensive
experiments show that our method performs favorably against the
state-of-the-art approaches on both synthetic and real-world datasets in terms
of perception metrics including NIQE, PI, and PIQE.
