Cloud Removal for Single Visible Image Based on Modified Dark Channel Prior with Multiple Scale
Shaoqi Shi, Ye Zhang, Xinyu Zhou, Jin Cheng · 2021
The cloud-contaminated phenomenon in the field of remote sensing has a serious impact on image processing so that a large number of images are unusable. To achieve cloud removal for single visible image, we propose a novel method based on modified dark channel prior with multiple scale (MDCPMS). In the structure of multiple scale, the cloudy image is firstly decomposed into high-frequency and low-frequency components. The former is uniformly amplified to enhance its weak contour, and the latter is processed by modified dark channel prior (DCP), whose estimation of atmospheric light is optimized for better cloud removal. Finally, the cloud-removed image is obtained through multi-scale reconstruction. Experimental results show that the proposed MDCPMS obtains a significant performance with slightest color distortion and is closest to the corresponding real image, compared with DCP and nonlocal method.