├── LICENSE ├── README.md └── images ├── Architecture ├── 1-way-GAN.png ├── 2-way-GAN.png ├── netD.png └── netG.png ├── Internet ├── CLHE │ ├── b0002.png │ ├── b0003.png │ ├── b0004.png │ ├── b0005.png │ ├── b0008.png │ ├── b0015.png │ ├── b0021.png │ ├── b0022.png │ ├── b0026.png │ ├── b0028.png │ ├── b0029.png │ ├── b0033.png │ └── b0034.png ├── DPED_Nexus5x │ ├── b0002.png │ ├── b0003.png │ ├── b0004.png │ ├── b0005.png │ ├── b0008.png │ ├── b0015.png │ ├── b0021.png │ ├── b0022.png │ ├── b0026.png │ ├── b0028.png │ ├── b0029.png │ ├── b0033.png │ └── b0034.png ├── DPED_iPhone6 │ ├── b0002.png │ ├── b0003.png │ ├── b0004.png │ ├── b0005.png │ ├── b0008.png │ ├── b0015.png │ ├── b0021.png │ ├── b0022.png │ ├── b0026.png │ ├── b0028.png │ ├── b0029.png │ ├── b0033.png │ └── b0034.png ├── DPED_iPhone7 │ ├── b0002.png │ ├── b0003.png │ ├── b0004.png │ ├── b0005.png │ ├── b0008.png │ ├── b0015.png │ ├── b0021.png │ ├── b0022.png │ ├── b0026.png │ ├── b0028.png │ ├── b0029.png │ ├── b0033.png │ └── b0034.png ├── Input │ ├── b0002.png │ ├── b0003.png │ ├── b0004.png │ ├── b0005.png │ ├── b0008.png │ ├── b0015.png │ ├── b0021.png │ ├── b0022.png │ ├── b0026.png │ ├── b0028.png │ ├── b0029.png │ ├── b0033.png │ └── b0034.png └── Our_HDR │ ├── b0002.png │ ├── b0003.png │ ├── b0004.png │ ├── b0005.png │ ├── b0008.png │ ├── b0015.png │ ├── b0021.png │ ├── b0022.png │ ├── b0026.png │ ├── b0028.png │ ├── b0029.png │ ├── b0033.png │ └── b0034.png ├── MIT498 ├── CLHE │ ├── a0212.png │ ├── a0481.png │ ├── a0535.png │ ├── a1305.png │ ├── a2995.png │ ├── a3203.png │ ├── a3451.png │ ├── a3552.png │ └── a4963.png ├── CycleGAN_HDR │ ├── a0212.png │ ├── a0481.png │ ├── a0535.png │ ├── a1305.png │ ├── a2995.png │ ├── a3203.png │ ├── a3451.png │ ├── a3552.png │ └── a4963.png ├── DPED_Nexus5x │ ├── a0212.png │ ├── a0481.png │ ├── a0535.png │ ├── a1305.png │ ├── a2995.png │ ├── a3203.png │ ├── a3451.png │ ├── a3552.png │ └── a4963.png ├── DPED_iPhone6 │ ├── a0212.png │ ├── a0481.png │ ├── a0535.png │ ├── a1305.png │ ├── a2995.png │ ├── a3203.png │ ├── a3451.png │ ├── a3552.png │ └── a4963.png ├── DPED_iPhone7 │ ├── a0212.png │ ├── a0481.png │ ├── a0535.png │ ├── a1305.png │ ├── a2995.png │ ├── a3203.png │ ├── a3451.png │ ├── a3552.png │ └── a4963.png ├── FLLF │ ├── a0212.png │ ├── a0481.png │ ├── a0535.png │ ├── a1305.png │ ├── a2995.png │ ├── a3203.png │ ├── a3451.png │ ├── a3552.png │ └── a4963.png ├── Input │ ├── a0212.png │ ├── a0481.png │ ├── a0535.png │ ├── a1305.png │ ├── a2995.png │ ├── a3203.png │ ├── a3451.png │ ├── a3552.png │ └── a4963.png ├── Label │ ├── a0212.png │ ├── a0481.png │ ├── a0535.png │ ├── a1305.png │ ├── a2995.png │ ├── a3203.png │ ├── a3451.png │ ├── a3552.png │ └── a4963.png ├── NPEA │ ├── a0212.png │ ├── a0481.png │ ├── a0535.png │ ├── a1305.png │ ├── a2995.png │ ├── a3203.png │ ├── a3451.png │ ├── a3552.png │ └── a4963.png ├── Our_HDR │ ├── a0212.png │ ├── a0481.png │ ├── a0535.png │ ├── a1305.png │ ├── a2995.png │ ├── a3203.png │ ├── a3451.png │ ├── a3552.png │ └── a4963.png ├── Our_SL │ ├── a0212.png │ ├── a0481.png │ ├── a0535.png │ ├── a1305.png │ ├── a2995.png │ ├── a3203.png │ ├── a3451.png │ ├── a3552.png │ └── a4963.png └── Our_UL │ ├── a0212.png │ ├── a0481.png │ ├── a0535.png │ ├── a1305.png │ ├── a2995.png │ ├── a3203.png │ ├── a3451.png │ ├── a3552.png │ └── a4963.png ├── Others ├── A-ex1-input.png ├── A-ex1-wiBN.png ├── A-ex1-woiBN.png ├── A-ex2-input.png ├── A-ex2-wiBN.png ├── A-ex2-woiBN.png ├── A-ex3-input.png ├── A-ex3-wiBN.png ├── A-ex3-woiBN.png ├── A-ex4-input.png ├── A-ex4-wiBN.png ├── A-ex4-woiBN.png ├── B-ex1-input.png ├── B-ex1-wiBN.png ├── B-ex1-woiBN.png ├── B-ex2-input.png ├── B-ex2-wiBN.png ├── B-ex2-woiBN.png ├── B-ex3-input.png ├── B-ex3-wiBN.png ├── B-ex3-woiBN.png ├── B-ex4-input.png ├── B-ex4-wiBN.png ├── B-ex4-woiBN.png ├── LSUN-bedrooms-awgan-64x64-lambda=0.1-iter=20k-3x3.png ├── LSUN-bedrooms-awgan-64x64-lambda=10-iter=20k-3x3.png ├── LSUN-bedrooms-awgan-64x64-lambda=1000-iter=20k-3x3.png ├── LSUN-bedrooms-wgan-gp-64x64-lambda=0.1-iter=20k-3x3.png ├── LSUN-bedrooms-wgan-gp-64x64-lambda=10-iter=20k-3x3.png ├── LSUN-bedrooms-wgan-gp-64x64-lambda=1000-iter=20k-3x3.png ├── Swissroll-lambda=0d1-iter=100-A-WGAN.png ├── Swissroll-lambda=0d1-iter=100-WGAN-GP.png ├── Swissroll-lambda=0d1-iter=2500-A-WGAN.png ├── Swissroll-lambda=0d1-iter=2500-WGAN-GP.png ├── Swissroll-lambda=0d1-iter=5000-A-WGAN.png ├── Swissroll-lambda=0d1-iter=5000-WGAN-GP.png ├── Swissroll-lambda=1-iter=100-A-WGAN.png ├── Swissroll-lambda=1-iter=100-WGAN-GP.png ├── Swissroll-lambda=1-iter=2500-A-WGAN.png ├── Swissroll-lambda=1-iter=2500-WGAN-GP.png ├── Swissroll-lambda=1-iter=5000-A-WGAN.png ├── Swissroll-lambda=1-iter=5000-WGAN-GP.png ├── Swissroll-lambda=10-iter=100-A-WGAN.png ├── Swissroll-lambda=10-iter=100-WGAN-GP.png ├── Swissroll-lambda=10-iter=2500-A-WGAN.png ├── Swissroll-lambda=10-iter=2500-WGAN-GP.png ├── Swissroll-lambda=10-iter=5000-A-WGAN.png ├── Swissroll-lambda=10-iter=5000-WGAN-GP.png ├── global-UNet-ex1-input.png ├── global-UNet-ex1-label.png ├── global-UNet-ex1-withG.png ├── global-UNet-ex1-woG.png ├── global-UNet-ex2-input.png ├── global-UNet-ex2-label.png ├── global-UNet-ex2-withG.png ├── global-UNet-ex2-woG.png ├── global-UNet-ex3-input.png ├── global-UNet-ex3-label.png ├── global-UNet-ex3-withG.png └── global-UNet-ex3-woG.png ├── poster-img.png ├── teaser.png └── youtube-thumbnail.png /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2019 Yu-Sheng Chen 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Deep Photo Enhancer: Unpaired Learning for Image Enhancement from Photographs with GANs 2 | 3 | ### [[Demo website]](http://www.cmlab.csie.ntu.edu.tw/project/Deep-Photo-Enhancer/) [[Youtube]](https://www.youtube.com/watch?v=d7OXb2sqoec) [[Paper]](https://www.cmlab.csie.ntu.edu.tw/project/Deep-Photo-Enhancer/CVPR-2018-DPE.pdf) [[Supplementary]](https://www.cmlab.csie.ntu.edu.tw/project/Deep-Photo-Enhancer/CVPR-2018-DPE-sm-compress.pdf) [[Download Demo Video]](https://www.cmlab.csie.ntu.edu.tw/project/Deep-Photo-Enhancer/enhance_video_crop.mp4) 4 | ### [[Spotlight Presentation-video]](https://www.youtube.com/watch?v=7y-zyzJXxxI) [[Spotlight Presentation-pdf]](https://www.cmlab.csie.ntu.edu.tw/project/Deep-Photo-Enhancer/CVPR-2018-DPE-spotlight-compress.pdf) [[Poster]](https://www.cmlab.csie.ntu.edu.tw/project/Deep-Photo-Enhancer/CVPR-2018-DPE-poster-compress.pdf) 5 | 6 |

7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 |

15 | TensorFlow implementation of the CVPR 2018 spotlight paper, Deep Photo Enhancer: Unpaired Learning for Image Enhancement from Photographs with GANs. If you use any code or data from our work, please cite our paper. 16 | 17 | ### [Update Jun. 05, 2019] Rename Model script 18 | I add the `rename_model.py` to the download link below. 19 | 20 | ### [Update Mar. 31, 2019] Inference Models (Supervsied and Unsupervised). 21 | Download link: [here](https://www.cmlab.csie.ntu.edu.tw/project/Deep-Photo-Enhancer/[Online_Demo_Models]_Deep-Photo-Enhancer.zip). The code is exactly the same I used in my demo website. (Sorry, I do not have time to polish it...) 22 | Simplified tutorial: Using the function `getInputPhoto` and `processImg` in the `TF.py` 23 | 24 | ### [Update Dec. 18, 2018] Data and Code (Supervsied and Unsupervised). 25 | There are too many people asked me to release the code even the code is not polished and is ugly as me. Therefore, I put my ugly code and the data [here](https://www.cmlab.csie.ntu.edu.tw/project/Deep-Photo-Enhancer/[Experimental_Code_Data]_Deep-Photo-Enhancer.zip). I also provide the [supervised code](https://www.cmlab.csie.ntu.edu.tw/project/Deep-Photo-Enhancer/[Experimental_Supervised_Code]_Deep-Photo-Enhancer.zip). There are a lot of unnecessary parts in the code. I will refactor the code ASAP. Regarding the data, I put the name of the images we used on [MIT-Adobe FiveK dataset](https://data.csail.mit.edu/graphics/fivek/). I directly used Lightroom to decode the images to TIF format and used Lightroom to resize the long side of the images to 512 resolution (The label images are from retoucher C). I am not sure whether I have right to release the HDR dataset we collected from [Flickr](https://www.flickr.com/search/?text=HDR) so I put the ID of them. You can download the images according to the IDs. (The code was run on 0.12 version of TensorFlow. The A-WGAN part in the code did not implement decreasing the lambda since the initial lambda was relatively small in that case.) 26 | 27 | Some useful issues: [#6](https://github.com/nothinglo/Deep-Photo-Enhancer/issues/6), [#16](https://github.com/nothinglo/Deep-Photo-Enhancer/issues/16), [#18](https://github.com/nothinglo/Deep-Photo-Enhancer/issues/18), [#24](https://github.com/nothinglo/Deep-Photo-Enhancer/issues/24), [#27](https://github.com/nothinglo/Deep-Photo-Enhancer/issues/27), [#38](https://github.com/nothinglo/Deep-Photo-Enhancer/issues/38), [#39](https://github.com/nothinglo/Deep-Photo-Enhancer/issues/39) 28 | 29 | ### Results 30 | 31 |

32 | 33 | | Method | Description | 34 | | :---: | --- | 35 | | Label | Retouched by photographer from MIT-Adobe 5K dataset [1] | 36 | | Our (HDR) | Our model trained on our HDR dataset with unpaired data | 37 | | Our (SL) | Our model trained on MIT-Adobe 5K dataset with paired data (supervised learning) | 38 | | Our (UL) | Our model trained on MIT-Adobe 5K dataset with unpaired data | 39 | | CycleGAN (HDR) | CycleGAN's model [2] trained on our HDR dataset with unpaired data | 40 | | DPED_device | DPED's model [3] trained on a specified device with paired data (supervised learning) | 41 | | CLHE | Heuristic method from [4] | 42 | | NPEA | Heuristic method from [5] | 43 | | FLLF | Heuristic method from [6] | 44 | 45 |

46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 |
InputLabelOur (HDR)
Our (SL)Our (UL)CycleGAN (HDR)
DPED_iPhone6DPED_iPhone7DPED_Nexus5x
CLHENPEAFLLF
89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 140 | 141 | 142 | 143 | 144 | 145 | 146 | 147 | 148 | 149 | 150 | 151 | 152 | 153 | 154 | 155 | 156 | 157 | 158 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | 168 | 169 | 170 | 171 | 172 | 173 | 174 | 175 | 176 | 177 | 178 | 179 | 180 | 181 | 182 | 183 | 184 | 185 | 186 | 187 | 188 | 189 | 190 | 191 | 192 | 193 | 194 | 195 | 196 | 197 | 198 | 199 | 200 | 201 | 202 | 203 | 204 | 205 | 206 | 207 | 208 | 209 | 210 | 211 | 212 | 213 | 214 | 215 | 216 | 217 | 218 | 219 | 220 | 221 | 222 | 223 | 224 | 225 | 226 | 227 | 228 |
Input (MIT-Adobe)Our (HDR)DPED_iPhone7CLHE
Input (Internet)Our (HDR)DPED_iPhone7CLHE
229 | 230 | ### User study 231 | 232 | 233 | 234 | 236 | 237 | 238 | 239 | 240 | 241 | 242 | 243 | 244 | 245 | 246 | 247 | 248 | 249 | 250 | 251 | 252 | 253 | 254 | 255 | 256 | 257 | 258 | 259 | 260 | 261 | 262 | 263 | 264 | 265 | 266 | 267 | 268 | 269 | 270 | 271 | 272 | 273 | 274 | 275 | 276 | 277 | 278 | 279 | 280 | 281 | 282 | 283 | 284 | 285 | 286 | 287 | 288 | 290 | 291 | 292 | 293 | 294 |
Preference Matrix
235 | (20 participants and 20 images using pairwise comparisons)
CycleGANDPEDNPEACLHEOursTotal
CycleGAN-3227231193
DPED368-14111929657
NPEA373259-14250824
CLHE377281258-77993
Ours389371350323- 289 | 1433
Our model trained on HDR images ranked the first and CLHE was the runner-up. When comparing our model with CLHE, 81% of users (323 among 400) preferred our results.
295 | 296 | ### Other applications of global U-Net, A-WGAN and iBN 297 | This paper proposes three improvements: global U-Net, adaptive WGAN (A-WGAN) and individual batch normalization (iBN). They generally improve results; and for some applications, the improvement is sufficient for crossing the bar and leading to success. We have applied them to some other applications. 298 | 299 | 300 | 301 | 302 | 303 | 304 | 305 | 306 | 307 | 308 | 309 | 310 | 311 | 312 | 313 | 314 | 315 | 316 | 317 | 318 | 319 | 320 | 321 | 322 | 323 | 324 | 325 | 327 | 328 |
InputGround truthglobal U-NetU-Net
For global U-Net, we applied it to trimap segmentation for pets using the Oxford-IIIT Pet dataset. The accuracies of U-Net and global U-Net are 0.8759 and 0.8905 respectively. 326 |
329 | 330 | 331 | 332 | 333 | 334 | 335 | 336 | 337 | 338 | 339 | 340 | 341 | 342 | 343 | 344 | 345 | 346 | 347 | 348 | 349 | 350 | 351 | 352 |
λ = 0.1λ = 10λ = 1000
WGAN-GP
A-WGAN
With different λ values, WGAN-GP could succeed or fail. The proposed A-WGAN is less dependent with λ and succeeded with all three λ values.
353 | 354 | 355 | 356 | 357 | 358 | 359 | 360 | 361 | 362 | 363 | 364 | 365 | 366 | 367 | 368 | 369 | 370 | 371 | 372 | 373 | 374 | 375 | 376 | 377 | 378 | 379 | 380 | 381 | 382 | 383 | 384 | 385 | 386 | 387 | 388 | 389 | 390 | 391 | 392 | 393 | 394 | 395 | 396 | 397 | 398 | 399 | 400 | 402 | 403 |
Male -> FemaleFemale -> Male
Inputwith iBNw/o iBNInputwith iBNw/o iBN
We applied the 2-way GAN to gender change of face images. As shown in the figure, the 2-way GAN failed on the task but succeeded after employing the proposed iBN. 401 |
404 | 405 | ### Architecture 406 | 407 | 408 | 409 | 410 | 411 | 412 | 413 | 414 | 415 | 416 | 417 | 418 | 419 | 420 | 421 | 422 | 423 | 424 | 425 | 426 | 427 | 428 |
Generator
Discriminator
1-way GAN2-way GAN
429 | 430 | ### Publication 431 | [Yu-Sheng Chen](https://www.cmlab.csie.ntu.edu.tw/~nothinglo/), [Yu-Ching Wang](https://www.cmlab.csie.ntu.edu.tw/~urchinwang/), [Man-Hsin Kao](https://www.cmlab.csie.ntu.edu.tw/~cindy0711/) and [Yung-Yu Chuang](https://www.csie.ntu.edu.tw/~cyy/). 432 | 433 | [National Taiwan University](https://www.ntu.edu.tw) 434 | 435 | Deep Photo Enhancer: Unpaired Learning for Image Enhancement from Photographs with GANs. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition 2018 (CVPR 2018), to appear, June 2018, Salt Lake City, USA. 436 | 437 | ### Citation 438 | ``` 439 | @INPROCEEDINGS{Chen:2018:DPE, 440 | AUTHOR = {Yu-Sheng Chen and Yu-Ching Wang and Man-Hsin Kao and Yung-Yu Chuang}, 441 | TITLE = {Deep Photo Enhancer: Unpaired Learning for Image Enhancement from Photographs with GANs}, 442 | YEAR = {2018}, 443 | MONTH = {June}, 444 | BOOKTITLE = {Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2018)}, 445 | PAGES = {6306--6314}, 446 | LOCATION = {Salt Lake City}, 447 | } 448 | ``` 449 | ### Reference 450 | 451 | > 1. *Bychkovsky, V., Paris, S., Chan, E., Durand, F.: Learning photographic global tonal adjustment with a database of input/output image pairs. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. pp. 97-104. CVPR'11 (2011)* 452 | > 2. *Zhu, J. Y., Park, T., Isola, P., Efros, A. A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the 2017 IEEE International Conference on Computer Vision. pp. 2242-2251. ICCV'17 (2017)* 453 | > 3. *Ignatov, A., Kobyshev, N., Vanhoey, K., Timofte, R., Van Gool, L.: DSLR-quality photos on mobile devices with deep convolutional networks. In: Proceedings of the 2017 IEEE International Conference on Computer Vision. pp. 3277-3285. ICCV'17 (2017)* 454 | > 4. *Wang, S., Cho, W., Jang, J., Abidi, M. A., Paik, J.: Contrast-dependent saturation adjustment for outdoor image enhancement. JOSA A. pp. 2532-2542. (2017)* 455 | > 5. *Wang, S., Zheng, J., Hu, H. M., Li, B.: Naturalness preserved enhancement algorithm for non-uniform illumination images. IEEE Transactions on Image Processing. pp. 3538-3548. TIP'13 (2013)* 456 | > 6. *Aubry, M., Paris, S., Hasinoff, S. W., Kautz, J., Durand, F.: Fast local laplacian filters: Theory and applications. ACM Transactions on Graphics. Article 167. TOG'14 (2014)* 457 | 458 | ### Contact 459 | Feel free to contact me if there is any questions (Yu-Sheng Chen nothinglo@cmlab.csie.ntu.edu.tw). 460 | -------------------------------------------------------------------------------- /images/Architecture/1-way-GAN.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/nothinglo/Deep-Photo-Enhancer/c8e94762a0fcfaccb25b890ee44275b4d4898e6d/images/Architecture/1-way-GAN.png -------------------------------------------------------------------------------- /images/Architecture/2-way-GAN.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/nothinglo/Deep-Photo-Enhancer/c8e94762a0fcfaccb25b890ee44275b4d4898e6d/images/Architecture/2-way-GAN.png -------------------------------------------------------------------------------- /images/Architecture/netD.png: 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