DehazeDCT: Towards Effective Non-Homogeneous Dehazing via Deformable Convolutional Transformer

Wei Dong, Han Zhou, Ruiyi Wang, Xiaohong Liu, Guangtao Zhai, Jun Chen; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 6405-6414

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 x6000) 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.