FPCNet

Fully Point-wise Convolutional Neural Network, ACM MM 2018.

Installation

install caffe master and compile matlab interface

Folder Structure

Root
    -data
        Folder containing hazy images
    -models
	    caffelmodel and deploy file of the FPCNet_DH
    -results
	    Folder containing dehazed results
    -utils
	    -fast-guided-filter
		    fast guided filter code[1]
	    -index
		    psnr and ssim code[2]
    -testDemoFPCNet_DH.m
    	main body of the demo script

Quick Start

testDemoFPCNet_DH.m
    -configure the following variables, 
        -"MatCaffeRoot":, path to the matlab interface of caffe
        -"gpu_mode"；1 for gpu and 0 for cpu

run testDemoFPCNet_DH.m

*you'll get the dehazed results in "./results/"

*The codes have been tested based on the Matlab Interface of CAFFE, which is compiled with Cuda8.0 and Cudnn v5 and Ubuntu 16.04.

Reference:

[1]. Fast guided filter, Kaiming He

[2]. A Benchmark for Single Image Dehazing, RESIDE

Citation

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

@inproceedings{zhang2018fpcnet,
  title={Fully Point-wise Convolutional Neural Network for Modeling Statistical Regularities in Natural Images},
  author={Zhang, Jing and Cao, Yang and Wang, Yang and Wen, Chenglin and Chen, Chang Wen},
  booktitle={ACM Multimedia Conference},
  year={2018}
}

Related Work

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

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

[3]. Nighttime haze removal based on a new imaging model, ICIP 2014. NighttimeDehaze: Project, NighttimeDehaze: github

chaimi2013/FPCNet