Two-Step Unsupervised Approach for Sand-Dust Image Enhancement

In sand-dust environments, light is scattered and absorbed, and sand-dust images thus suffer from severe image degradation problems, such as color shifts, low contrast, and blurred details. To address these problems, we propose a two-step unsupervised sand-dust image enhancement algorithm. In the first step, a convenient and competent color correction method is put forward to solve the color shift problem. Considering the wavelength attenuation features of sand-dust images, a linear stretching and blue channel compensation method is designed, and an adaptive color shift correction factor is developed to remove the color shift. In the second step, to enhance the clarity and details of the images, an unsupervised generative adversarial network is proposed, which does not require pairs of data for training. To reduce detail loss, the detail enhancement branch is designed, and the generator considers to more details through the constructed coarse-grained and fine-grained discriminators. The introduced multiscale perceptual loss promotes the image fidelity well. Experiments show that the proposed method achieves better color correction, enhances image details and clarity, has a better subjective effect, and outperforms existing sand-dust image enhancement methods both quantitatively and qualitatively. Similarly, our method promotes the application capability of the target detection algorithm and also has a good enhancement effect on underwater images and haze images.