Abstract: Due to the scattering and absorption of light by suspended particles,images captured in sand and dust conditions often suffer from a yellowish color bias and low contrast.Existing image enhancement algorithms for degraded images are mostly aimed at dehazing and deraining,making them difficult to effectively process sand dust images,thereby indicating a substantial potential for advancement in this field.The lack of large-scale datasets further complicates the task,hindering the ability of neural networks to robustly enhance sand dust images.To address this problem,a sand dust image enhancement method based on multi-cascaded attention interaction is proposed.In addition,we construct a new sand dust image dataset by combining the atmospheric scattering model and depth information.The method extracts multi-scale feature maps through an end-to-end U-Net model,fuses multi-scale feature maps using a multi-level channel attention interaction module,and enhances and restores detail information using a multi-scale convolution module.Experimental results show that the proposed method can effectively remove sand and dust in images and restore details,and achieves the best performance in terms of PSNR,SSIM,and IPLPS indices on the proposed dataset.

Key words: Sand dust images, Sand dust images enhancement, Sand dust images datasets, Feature fusion, Attention mechanism