DHFormer: A Vision Transformer-Based Attention Module for Image Dehazing
Abdul Wasi O. Jeba Shiney
Images acquired in hazy conditions have degradations induced in them.
Dehazing such images is a vexed and ill-posed problem. Scores of prior-based
and learning-based approaches have been proposed to mitigate the effect of haze
and generate haze-free images. Many conventional methods are constrained by
their lack of awareness regarding scene depth and their incapacity to capture
long-range dependencies. In this paper, a method that uses residual learning
and vision transformers in an attention module is proposed. It essentially
comprises two networks: In the first one, the network takes the ratio of a hazy
image and the approximated transmission matrix to estimate a residual map. The
second network takes this residual image as input and passes it through
convolution layers before superposing it on the generated feature maps. It is
then passed through global context and depth-aware transformer encoders to
obtain channel attention. The attention module then infers the spatial
attention map before generating the final haze-free image. Experimental
results, including several quantitative metrics, demonstrate the efficiency and
scalability of the suggested methodology.