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

• URL: http://arxiv.org/abs/2407.05169v1
• Date: Fri, 24 May 2024 10:59:18 GMT
• Title: DehazeDCT: Towards Effective Non-Homogeneous Dehazing via Deformable Convolutional Transformer
• Authors: Wei Dong, Han Zhou, Ruiyi Wang, Xiaohong Liu, Guangtao Zhai, Jun Chen,
• Abstract summary: We introduce an innovative non-homogeneous Dehazing method via Deformable Convolutional Transformer-like architecture (DehazeDCT) We first design a transformer-like network based on deformable convolution v4, which offers long-range dependency and adaptive spatial aggregation capabilities. Furthermore, we leverage a lightweight Retinex-inspired transformer to achieve color correction and structure refinement.
• Score: 43.807338032286346
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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