Haze and Thin Cloud Removal via Sphere Model Improved Dark Channel Prior

Abstract

Haze and cloud seriously degrade the quality of optical remote sensing images, which largely decrease their interpretability and intelligibility. In this letter, we propose a two-stage haze and thin cloud removal method based on homomorphic filtering (HF) and sphere model improved dark channel prior (DCP). Compared with current dehazing methods, the most advantage of the proposed method is that our method can deal with uneven haze, thick haze, and thin cloud. We observe that haze and cloud are highly related to the illumination component and mainly located in the low frequency of an image. Thus, we adapt HF to enhance the haze image, which makes the distribution of haze more even. In the second stage, we analyze the drawback of DCP, i.e., the transmission estimated by DCP is very sensitive to noise. To draw this issue, we propose a novel sphere model to estimate a more accurate transmission map. The sphere model improved DCP is more suitable for thick haze images than the traditional DCP. Extensive experimental results show that the proposed method significantly outperforms the compared state-of-the-art methods. The source code and data sets used in the letter are made public.