PM-LSMN: A Physical-Model-Based Lightweight Self-Attention Multiscale Net for Thin Cloud Removal

Recently, deep learning-based thin cloud removal methods have led to remarkable results. However, these deep learning models often have intricate structures, numerous parameters, and entail substantial training costs, rendering them impractical for widespread implementation in real-world applications. To overcome these challenges, a lightweight network for thin cloud removal, called a physical-model-based lightweight self-attention multiscale convolutional network (PM-LSMN), was proposed in this letter. The LSMN module integrates spatial attention mechanism, channel attention mechanism, and multiscale convolution net. This enhances the network’s ability to capture the distribution of thin clouds in the cloudy images. Based on the physical model of clouds in optical remote sensing images, the network performs element addition operations between the thin cloud distribution images and cloudy images to generate the final cloud-free image. To ensure color consistency in the reconstructed image, a color loss is incorporated into the design of the loss function. On the RICE1 datasets, the proposed method achieves satisfactory results with 25.90 dB in PSNR, 0.93 in SSIM, 4.115 in SAM, and 0.400 in ERGAS. In addition, the network accomplishes this with a more efficient parameter with 0.33 M in Params and computational footprint with 96.2 M in FLOPs. The code is available at
https://github.com/xizimi/PM-LSMN.git
.