Nighttime Haze Removal Based on a New Imaging Model, ICIP 2014.

The code has been tested on Win7/10 with Opencv 2.7.

Please install opencv 2.4.9 (or copy "opencv_core249.dll" "opencv_highgui249.dll " "opencv_imgproc249.dll" from "OPENCV_DIR/build/x64/vc10/bin/" to the same directory with "NighttimeDehaze.exe") before running this code.

Then, run the executable code as: "NighttimeDehaze.exe name.bmp", where "name.bmp" is the input nighttime hazy image, the output dehazed result is named as "name_J.bmp".

NighttimeDehaze
    -NighttimeDehaze.exe
        The executable code
    -*.dll
        The dependencies
    -flickr*.bmp
        Some test images
    -IMG*.bmp
        The test image with a color set and the corresponding ground truth (IMG_GT.bmp)

Please cite our paper in your publications if it helps your research:

@INPROCEEDINGS{JingZhang_ICIP2014_ND, 
	author={Zhang, Jing and Cao, Yang and Wang, Zengfu}, 
	booktitle={2014 21th IEEE International Conference on Image Processing (ICIP)}, 
	title={Nighttime Haze Removal Based on a New Imaging Model}, 
	year={2014}, 
	month={Oct}
}

[1]. Fast Haze Removal for Nighttime Image Using Maximum Reflectance Prior, CVPR 2017. MRP_CVPR: Project, MRP_CVPR: github

[2]. Fully Point-wise Convolutional Neural Network for Modeling Statistical Regularities in Natural Images, ACM MM 2018. FPC-Net: Project, FPC-Net: github

[3]. FAMED-Net: A Fast and Accurate Multi-scale End-to-end Dehazing Network, T-IP, 2019. FAMED-Net: Project, FAMED-Net: github

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