Post by ben on Aug 28, 2016 18:38:03 GMT
Check this out!
Image super-resolution through deep learning. This project uses deep learning to upscale 16x16 images by a 4x factor. The resulting 64x64 images display sharp features that are plausible based on the dataset that was used to train the neural net.
Here's an random, non cherry-picked, example of what this network can do. From left to right, the first column is the 16x16 input image, the second one is what you would get from a standard bicubic interpolation, the third is the output generated by the neural net, and on the right is the ground truth.
As you can see, the network is able to produce a very plausible reconstruction of the original face. As the dataset is mainly composed of well-illuminated faces looking straight ahead, the reconstruction is poorer when the face is at an angle, poorly illuminated, or partially occluded by eyeglasses or hands.
This particular example was produced after training the network for 3 hours on a GTX 1080 GPU, equivalent to 130,000 batches or about 10 epochs.
Instructions and the programs needed to do this yourself (Python3) are in the link: github.com/david-gpu/srez
Image super-resolution through deep learning. This project uses deep learning to upscale 16x16 images by a 4x factor. The resulting 64x64 images display sharp features that are plausible based on the dataset that was used to train the neural net.
Here's an random, non cherry-picked, example of what this network can do. From left to right, the first column is the 16x16 input image, the second one is what you would get from a standard bicubic interpolation, the third is the output generated by the neural net, and on the right is the ground truth.
As you can see, the network is able to produce a very plausible reconstruction of the original face. As the dataset is mainly composed of well-illuminated faces looking straight ahead, the reconstruction is poorer when the face is at an angle, poorly illuminated, or partially occluded by eyeglasses or hands.
This particular example was produced after training the network for 3 hours on a GTX 1080 GPU, equivalent to 130,000 batches or about 10 epochs.
Instructions and the programs needed to do this yourself (Python3) are in the link: github.com/david-gpu/srez
