Remote sensing image dehazing based on data blending and Laplace network

Deep learning-based dehazing of remote sensing images faces two major problems: distortion of remote sensing images and lack of large-scale real paired datasets. To solve these problems, we propose a remote sensing image dehazing method based on data mixing and Laplace network. The Laplace pyramid can divide remote sensing images into different frequency domain layers (the low-frequency layer retains global color information, and the high-frequency layer retains texture details from coarse to fine), and these features are fed into a lightweight multilayer perceptron to learn long-range dependencies. A backbone network consisting of a spatial weighted residual channel attention module can help the residual haze removal module to learn the distribution of haze in remote sensing images for effective haze removal. To address the problem of lack of large-scale real datasets, we cross-mix and restructure the synthetic dataset with the small-sample real dataset, and use the restructured mixed dataset for training, and the trained model can effectively recover the color information of real remote sensing images. After validating the effectiveness and superiority of our model on synthetic datasets, hybrid datasets, and synthetic hyperspectral datasets, we conduct generalizability experiments, and the results show the potential application of our method in advanced vision tasks.