Full Code of danaberman/non-local-dehazing for AI

Repository: danaberman/non-local-dehazing
Branch: master
Commit: 5922558ff1e5
Files: 13
Total size: 63.3 KB

Directory structure:
gitextract_35gc0cc4/

├── .gitignore
├── LICENSE.md
├── README.md
├── TR1000.txt
├── adjust.m
├── demo_non_local_dehazing.m
├── estimate_airlight.m
├── images/
│   ├── cityscape_params.txt
│   ├── forest_params.txt
│   ├── pumpkins_params.txt
│   └── train_params.txt
├── non_local_dehazing.m
└── wls_optimization.m

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FILE CONTENTS
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FILE: .gitignore
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*.asv

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FILE: LICENSE.md
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#LICENSE
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================================================
FILE: README.md
================================================
# Non-Local Image Dehazing

<img height="285px" width="200px" align="right" src="http://www.eng.tau.ac.il/~berman/images/cityscape_half.jpg">

This is the source code implementing the non-local single image dehazing algorithm described in the papers:

[Non-Local Image Dehazing. Berman, D. and Treibitz, T. and Avidan S., CVPR2016](https://www.eng.tau.ac.il/~berman/NonLocalDehazing/NonLocalDehazing_CVPR2016.pdf)

[Air-light Estimation using Haze-Lines. Berman, D. and Treibitz, T. and Avidan S., ICCP2017](https://www.eng.tau.ac.il/~berman/NonLocalDehazing/AirlightEstimation_ICCP2017.pdf)

If you use this code, please cite the appropriate paper.

#### bibtex

>    @inproceedings{NonLocalImageDehazing,    
>      title={Non-Local Image Dehazing},    
>      author={Berman, D. and Treibitz, T. and Avidan, S.},    
>      booktitle={IEEE Conf. CVPR},    
>      year={2016},
>    }
>
>    @inproceedings{AirlightEstimation,    
>      title={Air-light Estimation Using Haze-Lines},    
>      author={Berman, D. and Treibitz, T. and Avidan, S.},    
>      booktitle={IEEE Conf. ICCP},    
>      year={2017},
>    }




## Getting Started

### System Requirements:
The code requires MATLAB, and was tested on windows.

### Demo
1. Change MATLAB's directory to the folder the code was extracted to.
2. Run the file demo_non_local_dehazing.m in order to see the algorithm's
perfomance on the attached images.

### Dehazing other images
Each image in the demo is accompanied by a text file containing parameters. These value were taken from the [result pages of the paper: "Dehazing using Color-Lines"](http://www.cs.huji.ac.il/~raananf/projects/dehaze_cl/results/).

The gamma value is required since the image formation model applies to the linear images, where each pixel's value is directly related to the number of photons received at that location on the sensor. 
A non-linear tone-mapping is applied in camera and most of the images we see are not linear. The gamma value approximates the inverse mapping of the pixels' values to linear.

Despite that, **for new images, I recommend trying gamma=1 first** (this is the default). It often works even though it is not physically accurate.


## Authors
Dana Berman, *danamena@post.tau.ac.il*


## License
 
The software code of the non-local image dehazing algorithm is provided for non-commercial use under the attached [LICENSE.md](LICENSE.md)


================================================
FILE: TR1000.txt
================================================
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   -0.4928   -0.7773   -0.3912
   -0.4120   -0.8481   -0.3331
   -0.5297   -0.8322   -0.1641
   -0.4403    0.4121   -0.7977
   -0.4436   -0.8906   -0.1009
   -0.5405   -0.1069    0.8345
   -0.4518   -0.8919    0.0188
   -0.4301   -0.8760   -0.2182
   -0.4708   -0.8469    0.2473
   -0.3706   -0.9051    0.2085
   -0.3700   -0.8702    0.3252
   -0.5663   -0.7973    0.2086
   -0.5071   -0.7436    0.4359
    0.7099   -0.6764    0.1961
   -0.4382   -0.7243    0.5323
   -0.4713   -0.6390    0.6079
   -0.3625   -0.6928    0.6234
   -0.2775   -0.6581    0.6999
   -0.3046   -0.5659    0.7661
   -0.2131   -0.5183    0.8282
   -0.2473   -0.3094    0.9182
   -0.0222    0.9815    0.1900
   -0.0758   -0.3089   -0.9481
    0.5416    0.5314   -0.6514
   -0.9916    0.0770   -0.1040
   -0.1140   -0.5796   -0.8069
   -0.1952   -0.6296   -0.7520
   -0.2553   -0.7698   -0.5850
    0.1120    0.5000   -0.8588
   -0.1665   -0.8309   -0.5310
   -0.2817   -0.8264   -0.4875
   -0.2164   -0.9231   -0.3178
   -0.3056   -0.8724   -0.3815
   -0.3258   -0.9064   -0.2689
   -0.1397   -0.9814   -0.1314
   -0.3417   -0.9274   -0.1519
    0.3688   -0.9256    0.0851
   -0.3534   -0.9349   -0.0320
   -0.7076    0.2605    0.6568
   -0.2484   -0.9649   -0.0853
   -0.2651   -0.9531    0.1462
   -0.3643   -0.9269    0.0900
   -0.2692   -0.8925    0.3620
   -0.3948   -0.2837   -0.8738
   -0.9503    0.0483   -0.3074
   -0.3290   -0.7715    0.5445
   -0.2137   -0.8165    0.5362
   -0.2461   -0.7419    0.6237
    0.9062    0.1385    0.3996
   -0.1889   -0.6144    0.7661
   -0.0981   -0.5588    0.8235
   -0.1183   -0.4584    0.8808
   -0.1353   -0.3521    0.9261
    0.0475   -0.3508   -0.9352
   -0.0508   -0.4132   -0.9092
   -0.0245   -0.5115   -0.8589
   -0.9516   -0.2559    0.1704
   -0.1051   -0.6875   -0.7185
   -0.2257   -0.7038   -0.6736
   -0.1380   -0.7662   -0.6276
   -0.0767   -0.8832   -0.4627
    0.0175   -0.9175   -0.3974
   -0.1933   -0.8830   -0.4278
   -0.1029   -0.9276   -0.3592
   -0.0098   -0.9567   -0.2909
   -0.0292   -0.9840   -0.1756
    0.8777   -0.0262   -0.4785
   -0.2347   -0.9507   -0.2029
   -0.1501   -0.9886   -0.0139
    0.1662   -0.8336   -0.5267
   -0.0405   -0.9975   -0.0578
   -0.0508   -0.9834    0.1740
    0.0519   -0.9679    0.2459
   -0.1557   -0.9826    0.1017
    0.0371   -0.9317    0.3613
   -0.0723   -0.9124    0.4028
    0.0139   -0.8808    0.4734
   -0.0967   -0.8531    0.5127
   -0.0126   -0.8151    0.5792
   -0.1289   -0.7833    0.6082
   -0.0434   -0.7389    0.6724
   -0.0726   -0.6528    0.7541
    0.0163   -0.5916    0.8061
   -0.0223   -0.3890    0.9209
   -0.0388   -0.2781    0.9598
   -0.0018   -0.1375   -0.9905
   -0.0077   -0.6069   -0.7947
    0.0061   -0.6891   -0.7246
   -0.0237   -0.7624   -0.6466
   -0.0498   -0.8280   -0.5586
    0.0725   -0.8041   -0.5900
    0.0220    0.5772   -0.8163
   -0.1002   -0.1994   -0.9748
    0.0457   -0.8667   -0.4967
    0.1397   -0.8925   -0.4288
    0.2301   -0.9080   -0.3500
    0.0824   -0.9724   -0.2184
    0.1784   -0.9732   -0.1449
    0.0652   -0.9978    0.0141
   -0.1635   -0.9306    0.3275
    0.1691   -0.9823    0.0811
    0.0590   -0.9898    0.1300
    0.1603   -0.9671    0.1976
   -0.8609   -0.2341   -0.4517
    0.1467   -0.9377    0.3149
    0.1258   -0.8945    0.4290
   -0.5201    0.3190    0.7923
    0.0987   -0.8378    0.5370
    0.1813   -0.7840    0.5937
    0.0708   -0.7659    0.6390
    0.1533   -0.7060    0.6914
    0.0418   -0.6832    0.7290
   -0.0040   -0.4935    0.8697
    0.1100   -0.5226    0.8455
   -0.0854   -0.0362    0.9957
   -0.9573   -0.2404   -0.1603
    0.0732   -0.4517   -0.8892
    0.1894   -0.4854   -0.8535
    0.2057   -0.5827   -0.7862
    0.1020   -0.7311   -0.6746
    0.1950   -0.7634   -0.6158
    0.2848   -0.7876   -0.5464
    0.9667   -0.2206    0.1300
    0.2556   -0.8537   -0.4537
    0.3069   -0.9196   -0.2455
   -0.4356   -0.4065    0.8032
   -0.9165   -0.1621   -0.3658
    0.2811   -0.9530   -0.1128
    0.2758   -0.9611    0.0160
   -0.9243   -0.3671    0.1047
    0.1762   -0.9838   -0.0338
    0.2670   -0.9536    0.1392
    0.2534   -0.9316    0.2604
    0.3572   -0.9113    0.2049
    0.2336   -0.2057   -0.9503
   -0.0703    0.2818    0.9569
    0.2356   -0.8949    0.3791
    0.3205   -0.8390    0.4397
    0.2950   -0.7839    0.5463
    0.6090    0.3156    0.7276
    0.2617   -0.7197    0.6431
    0.2373   -0.6364    0.7340
    0.3336   -0.5625    0.7565
    0.1285   -0.6175    0.7760
    0.0913   -0.4211    0.9024
   -0.9823   -0.1656    0.0874
    0.1409   -0.2755   -0.9509
    0.1674   -0.3825   -0.9087
    0.2825   -0.4170   -0.8639
    0.3002   -0.5178   -0.8011
    0.3053   -0.6157   -0.7264
    0.4160    0.4393   -0.7962
    0.3978   -0.6410   -0.6564
    0.4836   -0.6581   -0.5771
    0.3357   -0.8673   -0.3675
    0.4483   -0.8108   -0.3764
    0.3766   -0.9131   -0.1564
    0.5231   -0.8049   -0.2802
    0.4717   -0.8599   -0.1952
    0.4709   -0.8785   -0.0807
    0.5626   -0.8263   -0.0268
    0.4680   -0.8831    0.0342
   -0.2701   -0.0820    0.9593
    0.6463   -0.7581   -0.0868
    0.4592   -0.8755    0.1505
    0.9705    0.0071    0.2409
    0.4429   -0.8560    0.2666
    0.4218   -0.8223    0.3820
    0.6836   -0.0874   -0.7246
    0.5943   -0.7131    0.3719
    0.4158    0.6789    0.6051
    0.4723   -0.6849    0.5548
    0.3433   -0.6455    0.6823
    0.4402   -0.6000    0.6679
   -0.4640   -0.8747    0.1404
    0.2225   -0.5455    0.8080
    0.1846   -0.3431    0.9210
    0.0725   -0.3126    0.9471
    0.1134   -0.1624   -0.9802
    0.2599   -0.3125   -0.9137
    0.3893   -0.4438   -0.8072
    0.3995   -0.5460   -0.7364
    0.4907   -0.5678   -0.6610
    0.5750   -0.5816   -0.5754
    0.5633   -0.6654   -0.4898
    0.4649   -0.7401   -0.4860
    0.6381   -0.6607   -0.3954
    0.5379   -0.7421   -0.4001
    0.6284   -0.7501   -0.2060
   -0.0006    0.0683   -0.9977
    0.5603   -0.8152   -0.1468
    0.7129   -0.6832   -0.1581
    0.7250   -0.6877   -0.0371
    0.5569   -0.8256    0.0907
    0.6465   -0.7623    0.0301
   -0.6025   -0.6878    0.4049
    0.6377   -0.7564    0.1456
    0.5437   -0.8134    0.2069
    0.5226   -0.7896    0.3217
    0.6883   -0.6565    0.3087
    0.4963   -0.7508    0.4358
    0.5605   -0.6724    0.4834
    0.6172   -0.5855    0.5256
    0.5299   -0.5994    0.5999
    0.4262   -0.5026    0.7521
    0.3171   -0.4712    0.8230
    0.2960   -0.3699    0.8806
    0.3254    0.3706    0.8700
    0.2734   -0.2613    0.9257
    0.0227   -0.2456   -0.9691
    0.3466   -0.2298   -0.9094
    0.3707   -0.3385   -0.8648
    0.4852   -0.4675   -0.7389
    0.5656   -0.3816   -0.7310
    0.5722   -0.4854   -0.6611
    0.6507   -0.4961   -0.5748
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    0.6536   -0.5861   -0.4788
    0.7227   -0.5897   -0.3604
    0.6922   -0.6657   -0.2786
    0.5536    0.7568   -0.3475
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    0.7870   -0.6081   -0.1043
    0.7939   -0.6078    0.0170
    0.8520   -0.5182    0.0741
   -0.0560   -0.1604    0.9855
    0.6203   -0.7401    0.2597
   -0.5210   -0.4451    0.7283
   -0.3986   -0.8014    0.4459
   -0.6233   -0.7740   -0.1118
    0.6569   -0.6272    0.4184
    0.6161   -0.5079    0.6020
    0.8124   -0.0097   -0.5830
    0.8995   -0.3259   -0.2910
    0.5899   -0.4194    0.6900
    0.4033   -0.3977    0.8241
    0.3820   -0.2891    0.8778
    0.3559   -0.1772    0.9176
    0.0528   -0.1969    0.9790
    0.3185   -0.1174   -0.9406
    0.4526   -0.2528   -0.8551
    0.4730   -0.3616   -0.8034
    0.7257   -0.4040   -0.5570
    0.5507   -0.2732   -0.7887
    0.6498   -0.3962   -0.6487
   -0.5597    0.1185   -0.8202
    0.8513   -0.2198   -0.4765
    0.5208   -0.5129    0.6824
    0.7924   -0.4048   -0.4563
    0.0445    0.6696   -0.7414
    0.7757   -0.4992   -0.3861
    0.8755   -0.4266   -0.2268
   -0.1600   -0.7031    0.6929
    0.8962   -0.4305   -0.1073
    0.8379   -0.5206   -0.1639
    0.9033   -0.4288    0.0128
    0.3760   -0.7087    0.5969
    0.8979   -0.4199    0.1319
    0.8503   -0.3829    0.3610
    0.8178   -0.4883    0.3046
    0.5998    0.0042    0.8001
    0.7826   -0.4640    0.4151
    0.7314   -0.4393    0.5217
    0.7479   -0.3310    0.5753
    0.6734   -0.4159    0.6112
    0.6680   -0.3180    0.6728
    0.5797   -0.3178    0.7503
    0.4998   -0.4132    0.7612
   -0.5288    0.5726    0.6265
    0.4338   -0.0891    0.8966
    0.2458   -0.1465    0.9582
    0.0798   -0.0428   -0.9959
    0.4256   -0.1407   -0.8939
    0.9894   -0.1356   -0.0516
    0.3743    0.9271    0.0177
    0.6213   -0.1816   -0.7622
    0.7071   -0.1975   -0.6790
    0.7839   -0.2094   -0.5845
    0.7935   -0.3097   -0.5239
    0.8974   -0.1292   -0.4220
    0.9054   -0.2280   -0.3581
    0.8548   -0.3171   -0.4108
    0.9433   -0.2341   -0.2352
    0.9269   -0.3341   -0.1711
    0.9649   -0.2374   -0.1122
    0.8805   -0.4041    0.2479
    0.9725   -0.2328    0.0104
    0.9411   -0.3345   -0.0494
    0.9899   -0.1241    0.0687
    0.9419   -0.3281    0.0716
    0.4439    0.0755   -0.8929
    0.9302   -0.3143    0.1898
   -0.2588   -0.9654    0.0323
    0.9053   -0.2956    0.3051
    0.8057   -0.3593    0.4710
    0.8132   -0.2509    0.5251
    0.7396   -0.2305    0.6324
    0.7213   -0.1275    0.6807
    0.6542   -0.2159    0.7249
   -0.7617    0.0246    0.6475
    0.5081    0.0016    0.8613
    0.9005    0.3921   -0.1881
    0.2894   -0.0006   -0.9572
    0.3935   -0.0238   -0.9190
    0.4940   -0.0464   -0.8682
    0.5929   -0.0697   -0.8023
    0.7363    0.0076   -0.6766
    0.7649   -0.1030   -0.6358
    0.8366   -0.1180   -0.5349
    0.7713    0.4042   -0.4916
    0.8043    0.3558    0.4760
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    0.9440   -0.1359   -0.3007
    0.9701   -0.0408   -0.2393
    0.9747   -0.1385   -0.1756
    0.9928   -0.0379   -0.1140
    0.9996   -0.0261    0.0061
    0.9957    0.0727   -0.0575
   -0.1601   -0.9633    0.2154
    0.9919   -0.0126    0.1261
    0.9762   -0.1091    0.1874
    0.9473   -0.2038    0.2472
    0.9144   -0.1837    0.3607
    0.9491   -0.0894    0.3019
    0.8665   -0.2742    0.4171
    0.8670   -0.1637    0.4707
    0.8020   -0.1479    0.5787
    0.8877    0.3550   -0.2931
    0.6933   -0.0221    0.7203
    0.6313   -0.1092    0.7678
   -0.0455   -0.9973    0.0585
    0.3250   -0.0616    0.9437
    0.1355   -0.1114    0.9845

================================================
FILE: adjust.m
================================================
function adj = adjust(img,percen)
%Contrast stretch utility function. Clip percen percentages of the pixels,
%while avoiding a drastic change to the white-balance.
% 
% This is a utility function used by the non-local image dehazing algorithm
% described in the paper:
% Non-Local Image Dehazing. Berman, D. and Treibitz, T. and Avidan S., CVPR2016,
% which can be found at:
% www.eng.tau.ac.il/~berman/NonLocalDehazing/NonLocalDehazing_CVPR2016.pdf
% If you use this code, please cite our paper.
% 
% The software code of the Non-Local Image Dehazing algorithm is provided
% under the attached LICENSE.md

if ~exist('percen','var') || isempty(percen), percen=[0.01 0.99]; end;

% linear contrast stretch to [0,1], identical on all colors
minn=min(img(:));
img=img-minn;
img=img./max(img(:));

% limit the change magnitude so the WB would not be drastically changed
contrast_limit = stretchlim(img,percen);
val = 0.2;
contrast_limit(2,:) = max(contrast_limit(2,:), 0.2);
contrast_limit(2,:) = val*contrast_limit(2,:) + (1-val)*max(contrast_limit(2,:), mean(contrast_limit(2,:)));
contrast_limit(1,:) = val*contrast_limit(1,:) + (1-val)*min(contrast_limit(1,:), mean(contrast_limit(1,:)));
adj=imadjust(img,contrast_limit,[],1);


================================================
FILE: demo_non_local_dehazing.m
================================================
% This is a demo script demonstrating the non-local image dehazing algorithm
% described in the paper:
% Non-Local Image Dehazing. Berman, D. and Treibitz, T. and Avidan S., CVPR2016,
% which can be found at:
% www.eng.tau.ac.il/~berman/NonLocalDehazing/NonLocalDehazing_CVPR2016.pdf
% If you use this code, please cite our paper.
% 
% Please read the instructions on README.md in order to use this code.
%
% Author: Dana Berman, 2016. 
% 
% The software code of the Non-Local Image Dehazing algorithm is provided
% under the attached LICENSE.md


% Choose image to use, four example image are supplied with the code in the
% sub-folder "images":
image_name = 'pumpkins'; % 'train'; % 'cityscape'; % 'forest'; % 
img_hazy = imread(['images/',image_name,'_input.png']);

% Load the gamma from the param file. 
% These values were given by Ra'anan Fattal, along with each image:
% http://www.cs.huji.ac.il/~raananf/projects/dehaze_cl/results/
fid = fopen(['images/',image_name,'_params.txt'],'r');
[C] = textscan(fid,'%s %f');
fclose(fid);
gamma = C{2}(1);

% Estimate air-light using our method described in:
% Air-light Estimation using Haze-Lines. Berman, D. and Treibitz, T. and 
% Avidan S., ICCP 2017
A = reshape(estimate_airlight(im2double(img_hazy).^(gamma)),1,1,3);

% Dehaze the image	
[img_dehazed, trans_refined] = non_local_dehazing(img_hazy, A, gamma );

% Display results
figure('Position',[50,50, size(img_hazy,2)*3 , size(img_hazy,1)]);
subplot(1,3,1); imshow(img_hazy);    title('Hazy input')
subplot(1,3,2); imshow(img_dehazed); title('De-hazed output')
subplot(1,3,3); imshow(trans_refined); colormap('jet'); title('Transmission')


================================================
FILE: estimate_airlight.m
================================================
function [ Aout ] = estimate_airlight( img, Amin, Amax, N, spacing, K, thres )
%Estimate airlight of an image, using a 3*2D Hough transform, where each
%point votes for a given location using a fixed set of angles.
%
%   This is an implementation of our paper:
%   Dana Berman, Tali Treibitz, Shai Avidan,
%   "Air-light Estimation using Haze-Lines", ICCP 2017
%   
%   Input arguments:
%   img - input image (mandatory)
%   Amin (Amax) - minimal (maximal) value if the air-light. Optional
%               Can be used to reduce the search space and save time
%               Either a scalar (identical for all color channels) or a 3
%               value vector (different range for each color channel)
%   N - number of colors clusters to use (the image is converted to an
%       indexed image of at most N different cluster). Optional
%   spacing - air-light candidates' resolution, 1/M in the paper. Optional
%   K - angular resolution. Optional
%   thres - cone resolution, optional, default recommended


%% Verify input params, set defaults when necessary (same as published results)
if ~exist('thres','var') || isempty(thres), thres = 0.01 ; end;
if ~exist('spacing','var') || isempty(spacing), spacing = 0.02 ; end; %1/M in the paper
if ~exist('n_colors','var') || isempty(N), N = 1000 ; end; %number of colors clusters
if ~exist('K','var') || isempty(K), K = 40 ; end; %number of angles

% Define search range for the air-light. The search range is different for each 
% color channel. These values were used in all of our experiments.
if ~exist('Amin','var') || isempty(Amin), Amin = [0,0.05,0.1]; end;
if ~exist('Amax','var') || isempty(Amax), Amax = 1; end;

% Air-light search range, accept a scalar if identical for all color channels
if isscalar(Amin), Amin = repmat(Amin,1,3); end 
if isscalar(Amax), Amax = repmat(Amax,1,3); end

%% Convert input image to an indexed image
[img_ind, points] = rgb2ind(img, N);
[h,w,~] = size(img);
% Remove empty clusters
idx_in_use = unique(img_ind(:));
idx_to_remove = setdiff(0:(size(points,1)-1),idx_in_use);
points(idx_to_remove+1,:) = [];
img_ind_sequential = zeros(h,w);
for kk = 1:length(idx_in_use)
    img_ind_sequential(img_ind==idx_in_use(kk)) = kk;
end
% Now the min value of img_ind_sequential is 1 rather then 0, and the indices
% correspond to points

% Count the occurences if each index - this is the clusters' weight
[points_weight,~] = histcounts(img_ind_sequential(:),size(points,1));
points_weight = points_weight./(h*w);
if ~ismatrix(points), points = reshape(points,[],3); end % verify dim

%% Define arrays of candidate air-light values and angles
angle_list = reshape(linspace(0, pi, K),[],1);
% Use angle_list(1:end-1) since angle_list(end)==pi, which is the same line
% in 2D as since angle_list(1)==0
directions_all = [sin(angle_list(1:end-1)) , cos(angle_list(1:end-1)) ];

% Air-light candidates in each color channel
ArangeR = Amin(1):spacing:Amax(1);
ArangeG = Amin(2):spacing:Amax(2);
ArangeB = Amin(3):spacing:Amax(3);

%% Estimate air-light in each pair of color channels
% Estimate RG
Aall = generate_Avals(ArangeR, ArangeG);
[~, AvoteRG] = vote_2D(points(:,1:2), points_weight, directions_all, Aall, thres );
% Estimate GB
Aall = generate_Avals(ArangeG, ArangeB);
[~, AvoteGB] = vote_2D(points(:,2:3), points_weight, directions_all, Aall, thres );
% Estimate RB
Aall = generate_Avals(ArangeR, ArangeB);
[~, AvoteRB] = vote_2D(points(:,[1,3]), points_weight, directions_all, Aall, thres);

%% Find most probable airlight from marginal probabilities (2D arrays)
% Normalize (otherwise the numbers are quite large)
max_val = max( [max(AvoteRB(:)) , max(AvoteRG(:)) , max(AvoteGB(:)) ]);
AvoteRG2 = AvoteRG./max_val;
AvoteGB2 = AvoteGB./max_val;
AvoteRB2 = AvoteRB./max_val;
% Generate 3D volumes from 3 different 2D arrays
A11 = repmat( reshape(AvoteRG2, length(ArangeG),length(ArangeR))', 1,1,length(ArangeB));
tmp = reshape(AvoteRB2, length(ArangeB),length(ArangeR))';
A22 = repmat(reshape(tmp, length(ArangeR),1,length(ArangeB)) , 1,length(ArangeG),1);
tmp2 = reshape(AvoteGB2, length(ArangeB),length(ArangeG))';
A33 = repmat(reshape(tmp2, 1, length(ArangeG),length(ArangeB)) , length(ArangeR),1,1);
AvoteAll = A11.*A22.*A33;
[~, idx] = max(AvoteAll(:));
[idx_r,idx_g,idx_b] = ind2sub([length(ArangeR),length(ArangeG),length(ArangeB)],idx);
Aout = [ArangeR(idx_r), ArangeG(idx_g), ArangeB(idx_b)];


end % function estimate_airlight_2D

%% Sub functions

function Aall = generate_Avals(Avals1, Avals2)
%Generate a list of air-light candidates of 2-channels, using two lists of
%values in a single channel each
%Aall's length is length(Avals1)*length(Avals2)
Avals1 = reshape(Avals1,[],1);
Avals2 = reshape(Avals2,[],1);
A1 = kron(Avals1, ones(length(Avals2),1));
A2 = kron(ones(length(Avals1),1), Avals2);
Aall = [A1, A2];
end % function generate_Avals

function [Aout, Avote2] = vote_2D(points, points_weight, directions_all, Aall, thres)
n_directions = size(directions_all,1);
accumulator_votes_idx = false(size(Aall,1), size(points,1), n_directions);
for i_point = 1:size(points,1)
    for i_direction = 1:n_directions
		 % save time and ignore irelevant points from the get-go
        idx_to_use = find( (Aall(:, 1) > points(i_point, 1)) & (Aall(:, 2) > points(i_point, 2)));
        if isempty(idx_to_use), continue; end
		
        % calculate distance between all A options and the line defined by
        % i_point and i_direction. If the distance is smaller than a thres,
        % increase the cell in accumulator
        dist1 = sqrt(sum([Aall(idx_to_use, 1)-points(i_point, 1), Aall(idx_to_use, 2)-points(i_point, 2)].^2,2));
        %dist1 = dist1 - min(dist1);
        dist1 = dist1./sqrt(2) + 1;
        
        dist =  -points(i_point, 1)*directions_all(i_direction,2) + ...
            points(i_point, 2)*directions_all(i_direction,1) + ...
            Aall(idx_to_use, 1)*directions_all(i_direction,2) - ...
            Aall(idx_to_use, 2)*directions_all(i_direction,1);
        idx = abs(dist)<2*thres.*dist1;
        if ~any(idx), continue; end

        idx_full = idx_to_use(idx);
        accumulator_votes_idx(idx_full, i_point,i_direction) = true;
    end
end
% use only haze-lined that are supported by 2 points or more
accumulator_votes_idx2 = (sum(uint8(accumulator_votes_idx),2))>=2; 
accumulator_votes_idx = bsxfun(@and, accumulator_votes_idx ,accumulator_votes_idx2);
accumulator_unique = zeros(size(Aall,1),1);
for iA = 1:size(Aall,1)
    idx_to_use = find(Aall(iA, 1) > points(:, 1) & (Aall(iA, 2) > points(:, 2)));
    points_dist = sqrt((Aall(iA,1) - points(idx_to_use,1)).^2+(Aall(iA,2) - points(idx_to_use,2)).^2);
    points_weight_dist = points_weight(idx_to_use).*(5.*exp(-reshape(points_dist,1,[]))+1); 
    accumulator_unique(iA) = sum(points_weight_dist(any(accumulator_votes_idx(iA,idx_to_use,:),3)));
end
[~, Aestimate_idx] = max(accumulator_unique);
Aout = Aall(Aestimate_idx,:);
Avote2 = accumulator_unique; 

end % function vote_2D


================================================
FILE: images/cityscape_params.txt
================================================
gamma 1.6
A1 0.81
A2 0.81
A3 0.82

================================================
FILE: images/forest_params.txt
================================================
gamma 1
A1 0.805
A2 0.817
A3 0.83

================================================
FILE: images/pumpkins_params.txt
================================================
gamma 1
A1 0.72
A2 0.785
A3 0.81

================================================
FILE: images/train_params.txt
================================================
gamma 1.5
A1 0.53
A2 0.53
A3 0.53

================================================
FILE: non_local_dehazing.m
================================================
function [img_dehazed, transmission] = non_local_dehazing(img_hazy, air_light, gamma)
%The core implementation of "Non-Local Image Dehazing", CVPR 2016
% 
% The details of the algorithm are described in our paper: 
% Non-Local Image Dehazing. Berman, D. and Treibitz, T. and Avidan S., CVPR2016,
% which can be found at:
% www.eng.tau.ac.il/~berman/NonLocalDehazing/NonLocalDehazing_CVPR2016.pdf
% If you use this code, please cite the paper.
%
%   Input arguments:
%   ----------------
%	img_hazy     - A hazy image in the range [0,255], type: uint8
%	air_light    - As estimated by prior methods, normalized to the range [0,1]
%	gamma        - Radiometric correction. If empty, 1 is assumed
%
%   Output arguments:
%   ----------------
%   img_dehazed  - The restored radiance of the scene (uint8)
%   transmission - Transmission map of the scene, in the range [0,1]
%
% Author: Dana Berman, 2016. 
%
% The software code of the Non-Local Image Dehazing algorithm is provided
% under the attached LICENSE.md


%% Validate input
[h,w,n_colors] = size(img_hazy);
if (n_colors ~= 3) % input verification
    error(['Non-Local Dehazing reuires an RGB image, while input ',...
        'has only ',num2str(n_colors),' dimensions']);
end

if ~exist('air_light','var') || isempty(air_light) || (numel(air_light)~=3)
    error('Dehazing on sphere requires an RGB airlight');
end

if ~exist('gamma','var') || isempty(gamma), gamma = 1; end

img_hazy = im2double(img_hazy);
img_hazy_corrected = img_hazy.^gamma; % radiometric correction


%% Find Haze-lines
% Translate the coordinate system to be air_light-centric (Eq. (3))
dist_from_airlight = double(zeros(h,w,n_colors));
for color_idx=1:n_colors
    dist_from_airlight(:,:,color_idx) = img_hazy_corrected(:,:,color_idx) - air_light(:,:,color_idx);
end

% Calculate radius (Eq. (5))
radius = sqrt( dist_from_airlight(:,:,1).^2 + dist_from_airlight(:,:,2).^2 +dist_from_airlight(:,:,3).^2 );

% Cluster the pixels to haze-lines
% Use a KD-tree impementation for fast clustering according to their angles
dist_unit_radius = reshape(dist_from_airlight,[h*w,n_colors]);
dist_norm = sqrt(sum(dist_unit_radius.^2,2));
dist_unit_radius = bsxfun(@rdivide, dist_unit_radius, dist_norm);
n_points = 1000;
% load pre-calculated uniform tesselation of the unit-sphere
fid = fopen(['TR',num2str(n_points),'.txt']);
points = cell2mat(textscan(fid,'%f %f %f')) ;
fclose(fid);
mdl = KDTreeSearcher(points);
ind = knnsearch(mdl, dist_unit_radius);


%% Estimating Initial Transmission

% Estimate radius as the maximal radius in each haze-line (Eq. (11))
K = accumarray(ind,radius(:),[n_points,1],@max);
radius_new = reshape( K(ind), h, w);
    
% Estimate transmission as radii ratio (Eq. (12))
transmission_estimation = radius./radius_new;

% Limit the transmission to the range [trans_min, 1] for numerical stability
trans_min = 0.1;
transmission_estimation = min(max(transmission_estimation, trans_min),1);


%% Regularization

% Apply lower bound from the image (Eqs. (13-14))
trans_lower_bound = 1 - min(bsxfun(@rdivide,img_hazy_corrected,reshape(air_light,1,1,3)) ,[],3);
transmission_estimation = max(transmission_estimation, trans_lower_bound);
 
% Solve optimization problem (Eq. (15))
% find bin counts for reliability - small bins (#pixels<50) do not comply with 
% the model assumptions and should be disregarded
bin_count       = accumarray(ind,1,[n_points,1]);
bin_count_map   = reshape(bin_count(ind),h,w);
bin_eval_fun    = @(x) min(1, x/50);

% Calculate std - this is the data-term weight of Eq. (15)
K_std = accumarray(ind,radius(:),[n_points,1],@std);
radius_std = reshape( K_std(ind), h, w);
radius_eval_fun = @(r) min(1, 3*max(0.001, r-0.1));
radius_reliability = radius_eval_fun(radius_std./max(radius_std(:)));
data_term_weight   = bin_eval_fun(bin_count_map).*radius_reliability;
lambda = 0.1;
transmission = wls_optimization(transmission_estimation, data_term_weight, img_hazy, lambda);


%% Dehazing
% (Eq. (16))
img_dehazed = zeros(h,w,n_colors);
leave_haze = 1.06; % leave a bit of haze for a natural look (set to 1 to reduce all haze)
for color_idx = 1:3
    img_dehazed(:,:,color_idx) = ( img_hazy_corrected(:,:,color_idx) - ...
        (1-leave_haze.*transmission).*air_light(color_idx) )./ max(transmission,trans_min);
end

% Limit each pixel value to the range [0, 1] (avoid numerical problems)
img_dehazed(img_dehazed>1) = 1;
img_dehazed(img_dehazed<0) = 0;
img_dehazed = img_dehazed.^(1/gamma); % radiometric correction

% For display, we perform a global linear contrast stretch on the output, 
% clipping 0.5% of the pixel values both in the shadows and in the highlights 
adj_percent = [0.005, 0.995];
img_dehazed = adjust(img_dehazed,adj_percent);

img_dehazed = im2uint8(img_dehazed);

end % function non_local_dehazing


================================================
FILE: wls_optimization.m
================================================
function out = wls_optimization(in, data_weight, guidance, lambda)
%Weighted Least Squares optimization solver.
% Given an input image IN, we seek a new image OUT, which, on the one hand,
% is as close as possible to IN, and, at the same time, is as smooth as
% possible everywhere, except across significant gradients in the hazy image.
%
%  Input arguments:
%  ----------------
%  in             - Input image (2-D, double, N-by-M matrix).   
%  data_weight    - High values indicate it is accurate, small values
%                   indicate it's not.
%  guidance       - Source image for the affinity matrix. Same dimensions
%                   as the input image IN. Default: log(IN)
%  lambda         - Balances between the data term and the smoothness
%                   term. Increasing lambda will produce smoother images.
%                   Default value is 0.05 
%
% This function is based on the implementation of the WLS Filer by Farbman,
% Fattal, Lischinski and Szeliski, "Edge-Preserving Decompositions for 
% Multi-Scale Tone and Detail Manipulation", ACM Transactions on Graphics, 2008
% The original function can be downloaded from: 
% http://www.cs.huji.ac.il/~danix/epd/wlsFilter.m


small_num = 0.00001;

if ~exist('lambda','var') || isempty(lambda), lambda = 0.05; end

[h,w,~] = size(guidance);
k = h*w;
guidance = rgb2gray(guidance);

% Compute affinities between adjacent pixels based on gradients of guidance
dy = diff(guidance, 1, 1);
dy = -lambda./(sum(abs(dy).^2,3) + small_num);
dy = padarray(dy, [1 0], 'post');
dy = dy(:);

dx = diff(guidance, 1, 2); 
dx = -lambda./(sum(abs(dx).^2,3) + small_num);
dx = padarray(dx, [0 1], 'post');
dx = dx(:);


% Construct a five-point spatially inhomogeneous Laplacian matrix
B = [dx, dy];
d = [-h,-1];
tmp = spdiags(B,d,k,k);

ea = dx;
we = padarray(dx, h, 'pre'); we = we(1:end-h);
so = dy;
no = padarray(dy, 1, 'pre'); no = no(1:end-1);

D = -(ea+we+so+no);
Asmoothness = tmp + tmp' + spdiags(D, 0, k, k);

% Normalize data weight
data_weight = data_weight - min(data_weight(:)) ;
data_weight = 1.*data_weight./(max(data_weight(:))+small_num);

% Make sure we have a boundary condition for the top line:
% It will be the minimum of the transmission in each column
% With reliability 0.8
reliability_mask = data_weight(1,:) < 0.6; % find missing boundary condition
in_row1 = min( in,[], 1);
data_weight(1,reliability_mask) = 0.8;
in(1,reliability_mask) = in_row1(reliability_mask);

Adata = spdiags(data_weight(:), 0, k, k);

A = Adata + Asmoothness;
b = Adata*in(:);

% Solve
% out = lsqnonneg(A,b);
out = A\b;
out = reshape(out, h, w);