MCADNet: A Multi-Scale Cross-Attention Network for Remote Sensing Image Dehazing

Tao Tao, Haoran Xu, Xin Guan and Hao Zhou ()
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Tao Tao: School of Computer Science and Technology, Anhui University of Technology, Ma’anshan 243002, China
Haoran Xu: School of Computer Science and Technology, Anhui University of Technology, Ma’anshan 243002, China
Xin Guan: College of Computer and Information Science, Southwest University, Chongqing 400715, China
Hao Zhou: School of Computer Science and Technology, Anhui University of Technology, Ma’anshan 243002, China

Mathematics, 2024, vol. 12, issue 23, 1-17

Abstract: Remote sensing image dehazing (RSID) aims to remove haze from remote sensing images to enhance their quality. Although existing deep learning-based dehazing methods have made significant progress, it is still difficult to completely remove the uneven haze, which often leads to color or structural differences between the dehazed image and the original image. In order to overcome this difficulty, we propose the multi-scale cross-attention dehazing network (MCADNet), which offers a powerful solution for RSID. MCADNet integrates multi-kernel convolution and a multi-head attention mechanism into the U-Net architecture, enabling effective multi-scale information extraction. Additionally, we replace traditional skip connections with a cross-attention-based gating module, enhancing feature extraction and fusion across different scales. This synergy enables the network to maximize the overall similarity between the restored image and the real image while also restoring the details of the complex texture areas in the image. We evaluate MCADNet on two benchmark datasets, Haze1K and RICE, demonstrating its superior performance. Ablation experiments further verify the importance of our key design choices in enhancing dehazing effectiveness.

Keywords: image dehazing; remote sensing; multi-scale feature extraction; cross-attention mechanism (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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