Make your pictures beautiful with a touch of deep learning magic

This is exactly the data we want for such an enhancement task: input a low-quality image (from the phone) and have the deep network try to predict what the high-quality version (from the DSLR) would look like.An image has several attributes that we may want to enhance: lighting, colors, texture, contrast, and sharpness..The deep network is trained to hit on all of these attributes with four different loss functions:Color loss: euclidean distance between the blurred versions of the predicted and target images.Texture loss: based on the classification loss from a Generative Adversarial Network (GAN)..The GAN is trained to predict whether a grayscale photo is of high or low quality..Since grayscale is used, the network will be nicely focused on the textures of the image rather than color.Content loss: difference between VGG features of the predicted image and the ground truth..This loss ensures that the overall structure and objects in the image (i.e image semantics) remain the same.Total Variation loss: total vertical and horizontal gradients in the image..This enforces smoothness in the image, such that the final result is not too grainy or noisy.Finally, these losses are all added up and an end-to-end network is trained to make the prediction!Architecture from the paperThe codeThanks to the beauty of the open source mindset in the AI community, there is a publicly available implementation of this photo enhancer right here!.Here’s how you can use it.First clone the repositorygit clone the required librariespip install tensorflow-gpupip install numpypip install scipyAll of the pre-trained models already come with the repository in the models_orig folder, so there’s no need to download them!Place the photos you want to enhance in the following directory:dped/iphone/test_data/full_size_test_images/This is the default directory for “iphone”, but you can change the code in the script if you want to change it..The reason it says “iphone” is because the authors originally trained 3 separate models using photos from 3 smartphones: iphone, sony, and blackberry, so those are your three options..But the model works quite well on most photos with any of these options, so we can just pick one and run with it!Finally, to enhance the photos we just run one simple script:python model=iphone_orig test_subset=full Voila!.Your enhanced and professional-looking photos will be saved in the visual_results folder!Give the code a try yourself, it’s great fun!.See how your photos look after the enhancement..Feel free to post a link below to share your photos with the community.. More details

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