Fast Hyperspectral Image Dehazing With Dark-Object Subtraction Model

Zhen Li, Puhong Duan, Shangsong Hu, Muxing Li, Xudong Kang

Abstract: Haze in hyperspectral images (HSIs) is a common phenomenon, which severely degrades the quality of the acquired data and limits its applications. In this letter, a dark-object subtraction model is proposed for haze removal of HSIs, which mainly is composed of three steps. First, a haze density map is estimated according to the haze characteristic in different spectral channels. Then, we design a saliency measure method to automatically calculate the haze abundance of different channels. Finally, the haze-free image is obtained by solving the dark-object subtraction model. Experiments on real and simulated datasets demonstrate that our method consistently outperforms other state-of-the-art dehazing techniques in terms of reconstructed performance and computational cost.

External IDs:dblp:journals/lgrs/LiDHLK22