A Remote Sensing Image Dehazing Method Based on Heterogeneous Priors
Shan Liang, Tao Gao, Ting Chen, Peng Cheng
Abstract
Remote sensing image dehazing is crucial for both military and civil applications. However, dehazed remote sensing images often suffer from pronounced artifacts and tend to overestimate the atmospheric light value. We propose a novel dehazing method based on heterogeneous priors. Specifically, superpixels are extracted from the hazy remote sensing image using a depth-based simple linear iterative clustering superpixel segmentation (DSLIC) algorithm. These superpixels serve as cells for transmission and atmospheric light estimation. To improve the robustness of atmospheric light estimation, we develop an atmospheric light value-map fusion estimation (ALFE) model that integrates the heterogeneous priors-guided haze concentration model (HP-HCM) to derive the global atmospheric light value, while utilizing the bright channel value within each superpixel as the local atmospheric light map. We also introduce a dynamic dehazing intensity parameter (DDIP) model, which refine the transmission map based on the HP-HCM. Extensive comparative experiments validate the superior performance of the proposed method. The PSNR and SSIM achieved by our method exceed those of the dark channel prior (DCP) by 22.2% and 37.5%, respectively.