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Jiang, B., Meng, H., Ma, X., Wang, L., Zhou, Y., Xu, P.F., Jiang, S.Y. and Meng, X.J. (2018) Nighttime Image Dehazing with Modified Models of Color Transfer and Guided Image Filter. Multimedia Tools and Applications, 77, 3125-3141.
https://doi.org/10.1007/s11042-017-4954-9

has been cited by the following article:

• TITLE: A Novel Method for Night-Time Single Image Dehazing

  AUTHORS: Prince Owusu-Agyeman, Xie Wei, James Okae

  KEYWORDS: Nighttime, Haze Removal, Dark Channel Prior, Image Enhancement, Machine Vision

  JOURNAL NAME: Journal of Computer and Communications, Vol.7 No.11, November 20, 2019

  ABSTRACT: Images acquired under deprived weather environment are frequently corrupted due to the presence of haze, mist, fog or other aerosols in a form of noise. Haze elimination is essential in computer vision and computational photography applications. Generally, there is the existence of numerous approaches towards haze removal which are mostly meant for hazy images under daytime environments. Although the potency of these proposed approaches has been comprehensively established on daylight hazy images. However these procedures inherit significant limitations on images influenced by night-time hazy environments. Since night time haze removal dehazing remains an ill-posed problem, we proposed a novel method for night-time single image dehazing which is efficient under night-time environments. The proposed scheme is a dark channel-based local image dehazing procedure that locally estimates the atmospheric intensity for each selected mask on a corrupted image independently and not the entire image. This is done in order to overcome the challenge of night-scenes that are exposed to multiple/artificial lights source and spatially non-uniform environmental illumination. We performed an adaptive filtering on the combined dehazed masks to improve the degraded image. We validated the supremacy of the proposed approach in terms of speed and robustness through computer-based experiments. Conclusively, we displayed comparison results with state-of-the-art and extensively emphasized the comparative advantage of our scheme.