Generate photos based on over 1M protos processed by ourselves before

Question

We are running a huge team that process child photos for our customers, the team processes over 1M photos per year.

The process includes basic tuning of light, resize, apply some filters to make the skin looks better.

We want to use deep learning to complete the jobs as much as possible. Which means I want to choose one model and train that model using our existing data. And then use the trained model to generate photos by inputing the new unprocessed photos.

Is there existing model that I can make use of, or any papers have covered this scenario?

Any help would be appreciated, thanks!

Answer 1

You could try something like this: https://arxiv.org/pdf/1412.7725.pdf. But with deep learning and your amount of training data you can problem get any big enough model to work well.

Answer 2

Image generation is not what you should search for. Image generation means that an image is generated (almost) completely from nothing. You want to enhance an existing image.

Although I haven't read any papers about this scenario so far, searching for "image enhancement neural network" reveald several promising results:

• A Survey on Image Enhancement Techniques: Classical Spatial Filter, Neural Network, Cellular Neural Network, and Fuzzy Filter: http://ieeexplore.ieee.org/document/4237993/
• A new class of nonlinear filters for image enhancement: http://ieeexplore.ieee.org/document/150915/
• An image enhancement technique combining sharpening and noise reduction: http://ieeexplore.ieee.org/document/1044761/

I guess you could do the following:

Create a CNN model. The only "special" thing of this model is that it does not have a fully connected layer as target, but another (3 channel) image. You have to adjust the error function to this. (Similar to semantic segmentation).