Dehazing of Panchromatic Remote Sensing Images Based on Histogram Features

During long-range imaging, the turbid medium in the atmosphere absorbs and scatters light, resulting in reduced contrast, a narrowed dynamic range, and obscure detail information in remote sensing images. The prior-based method has the advantages of good real-time performance and a wide application range. However, few of the existing prior-based methods are applicable to the dehazing of panchromatic images. In this paper, we innovatively propose a prior-based dehazing method for panchromatic remote sensing images through statistical histogram features. First, the hazy image is divided into plain image patches and mixed image patches according to the histogram features. Then, the features of the average occurrence differences between adjacent gray levels (AODAGs) of plain image patches and the features of the average distance to the gray-level gravity center (ADGG) of mixed image patches are, respectively, calculated. Then, the transmission map is obtained according to the statistical relation equation. Then, the atmospheric light of each image patch is calculated separately based on the maximum gray level of the image patch using the threshold segmentation method. Finally, the dehazed image is obtained based on the physical model. Extensive experiments in synthetic and real-world panchromatic hazy remote sensing images show that the proposed algorithm outperforms state-of-the-art dehazing methods in both efficiency and dehazing effect.