  Image dehazing, a pivotal task in low-level vision, aims to restore the
visibility and detail from hazy images. Many deep learning methods with
powerful representation learning capability demonstrate advanced performance on
non-homogeneous dehazing, however, these methods usually struggle with
processing high-resolution images (e.g., $4000 \times 6000$) due to their heavy
computational demands. To address these challenges, we introduce an innovative
non-homogeneous Dehazing method via Deformable Convolutional Transformer-like
architecture (DehazeDCT). Specifically, we first design a transformer-like
network based on deformable convolution v4, which offers long-range dependency
and adaptive spatial aggregation capabilities and demonstrates faster
convergence and forward speed. Furthermore, we leverage a lightweight
Retinex-inspired transformer to achieve color correction and structure
refinement. Extensive experiment results and highly competitive performance of
our method in NTIRE 2024 Dense and Non-Homogeneous Dehazing Challenge, ranking
second among all 16 submissions, demonstrate the superior capability of our
proposed method. The code is available:
https://github.com/movingforward100/Dehazing_R.
