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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Software and pre-trained models for automatic photo quality enhancement using Deep Convolutional Networks
| Date | Stars |
|---|---|
| 2026-07-24 | 1709 |
| 2026-07-25 | 1709 |
| 2026-07-28 | 1709 |
| 2026-07-30 | 1709 |
| 2026-08-06 | 1709 |
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## DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks <br/> <img src="https://aiff22.github.io/assets/img/teaser_git.jpg"/> <br/> #### 1. Overview [[Paper]](https://arxiv.org/pdf/1704.02470.pdf) [[Project webpage]](https://aiff22.github.io/) [[Enhancing RAW photos]](https://github.com/aiff22/PyNET) [[Rendering Bokeh Effect]](https://github.com/aiff22/PyNET-Bokeh) The provided code implements the paper that presents an end-to-end deep learning approach for translating ordinary photos from smartphones into DSLR-quality images. The learned model can be applied to photos of arbitrary resolution, while the methodology itself is generalized to any type of digital camera. More visual results can be found [here](https://aiff22.github.io/#demo). #### 2. Prerequisites - Python + Pillow, scipy, numpy, imageio packages - [TensorFlow 1.x / 2.x](https://www.tensorflow.org/install/) + [CUDA CuDNN](https://developer.nvidia.com/cudnn) - Nvidia GPU #### 3. First steps - Download the pre-trained [VGG-19 model](https://download.ai-benchmark.com/s/CCDiWM2sE25x2dW/download/imagenet-vgg-verydeep-19.mat) and put it into `vgg_pretrained/` folder - Download [DPED dataset](https://aiff22.github.io/#dataset) (patches for CNN training) and extract it into `dped/` folder. <sub>This folder should contain three subolders: `sony/`, `iphone/` and `blackberry/`</sub> <br/> #### 4. Train the model ```bash python train_model.py model=<model> ``` Obligatory parameters: >```model```: **```iphone```**, **```blackberry```** or **```sony```** Optional parameters and their default values: >```batch_size```: **```50```** - batch size [smaller values can lead to unstable training] <br/> >```train_size```: **```30000```** - the number of training patches randomly loaded each ```eval_step``` iterations <br/> >```eval_step```: **```1000```** - each ```eval_step``` iterations the model is saved and the training data is reloaded <br/> >```num_train_iters```: **```20000```** - the number of training iterations <br/> >```learning_rate```: **```5e-4```** - learning rate <br/> >```w_content```: **```10```** - the weight of the content loss <br/> >```w_color```: **```0.5```** - the weight of the color loss <br/> >```w_texture```: **```1```** - the weight of the texture [adversarial] loss <br/> >```w_tv```: **```2000```** - the weight of the total variation loss <br/> >```dped_dir```: **```dped/```** - path to the folder with DPED dataset <br/> >```vgg_dir```: **```vgg_pretrained/imagenet-vgg-verydeep-19.mat```** - path to the pre-trained VGG-19 network <br/> Example: ```bash python train_model.py model=iphone batch_size=50 dped_dir=dped/ w_color=0.7 ``` <br/> #### 5. Test the provided pre-trained models ```bash python test_model.py model=<model> ``` Obligatory parameters: >```model```: **```iphone_orig```**, **```blackberry_orig```** or **```sony_orig```** Optional parameters: >```test_subset```: **```full```**,**```small```** - all 29 or only 5 test images will be processed <br/> >```resolution```: **```orig```**,**```high```**,**```medium```**,**```small```**,**```tiny```** - the resolution of the test images [**```orig```** means original resolution]<br/> >```use_gpu```: **```true```**,**```false```** - run models on GPU or CPU <br/> >```dped_dir```: **```dped/```** - path to the folder with DPED dataset <br/> Example: ```bash python test_model.py model=iphone_orig test_subset=full resolution=orig use_gpu=true ``` <br/> #### 6. Test the obtained models ```bash python test_model.py model=<model> ``` Obligatory parameters: >```model```: **```iphone```**, **```blackberry```** or **```sony```** Optional parameters: >```test_subset```: **```full```**,**```small```** - all 29 or only 5 test images will b
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matched fp:b0b0192921ec147e, topic:deep-learning
matched fp:b0b0192921ec147e, topic:computer-vision
matched fp:b0b0192921ec147e, topic:gan