Dehazing network based on haze density

Yanling Hua; Zhengrong Zuo

doi:10.1117/12.2538200

14 February 2020 Dehazing network based on haze density

Yanling Hua, Zhengrong Zuo

Proceedings Volume 11430, MIPPR 2019: Pattern Recognition and Computer Vision; 114300V (2020) https://doi.org/10.1117/12.2538200
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

Abstract

Single image dehazing is a challenging ill-posed restoration problem. Most of dehazing algorithms follow the classical atmospheric scattering model and adopt same parameters for different hazy density areas in hazy images. In this paper, we proposed an end-to-end dehazing algorithm, called Dehazing Network based on Haze Density(DNBHD). The proposed network involves a haze density map estimation network and a dehazing network. By the estimated haze density map, hazy image is divided into a mist region and a dense fog region which are respectively feed into dehazing network. Compared with previous dehazing algorithm, DNBHD is independent on the atmospheric scattering model, and considers uniform fog distribution in images. We use different parameters to handle different hazy density regions, avoiding color distortion and inappropriate brightness caused by overall defogging. The experiments show our algorithm achieves significant improvements over the state-of-the-art methods

Citation Download Citation

Yanling Hua and Zhengrong Zuo "Dehazing network based on haze density", Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114300V (14 February 2020); https://doi.org/10.1117/12.2538200

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