Nighttime Image-Dehazing: A Review and Quantitative Benchmarking


Visibility enhancement of images captured during hazy weather conditions is highly essential for various important applications like intelligent vehicles, surveillance, remote sensing, etc. In recent years, researchers proposed numerous image-dehazing methods mostly focusing on daytime images’ characteristics. In this work, we have highlighted the dissimilarities among the characteristics of daytime and nighttime hazy images and explained that well-known daytime image-dehazing priors cannot dehaze nighttime hazy images effectively. Following this discussion, we have provided a comprehensive review of existing nighttime image-dehazing methods after grouping them according to different nighttime hazy image models based on which they were designed as their methodologies vastly vary with those models. Thereafter, we have performed comparative qualitative and quantitative analyses of outputs obtained by applying these methods on images belonging to novel N-HAZE database. N-HAZE comprises of both indoor and outdoor real-world nighttime hazy images captured in the presence of haze created by artificial haze machines and corresponding Ground Truth images. Finally, we have concluded our work by stating the existing challenges and future scope of work in this field after analyzing the strengths and limitations of each method. Our main aim behind conducting this survey is to draw the attention of more researchers towards this less explored yet significant research topic and encourage them to design new methods which can solve the existing challenges. To the best of our knowledge, we are the first ones to review the nighttime image-dehazing methods and to design N-HAZE, which is the first database designed for benchmarking these methods.

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National Highway Traffic Safety Administration


Dark Channel Prior


Ground Truth


Structural Similarity Index


Mean Square Error


Peak Signal-to-Noise Ratio


Fog Aware Density Evaluator


Guided Image Filter


Color Channel Transfer


Maximum Reflectance Prior


Dilation Factor


Bright Channel Prior


Haze Density Prediction


Rectified Linear Unit


Haze Density Prediction Network


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Correspondence to Sriparna Banerjee.

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Banerjee, S., Sinha Chaudhuri, S. Nighttime Image-Dehazing: A Review and Quantitative Benchmarking. Arch Computat Methods Eng (2020).

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  • Nighttime image-dehazing survey
  • Dark Channel Prior
  • Spatially varying illumination characteristics
  • Glow characteristics
  • Nighttime hazy image models
  • N-HAZE database