U-Net: Binary to Sketch
An attempt at expanding deepcolor to refining drawings; maps a binary image (pixel values 0, 255) to a greyscale tone image (a "sketch" with tones between 0 and 255). The network is trained with a dataset of ~5800 images collected from /r/awwnime for 80 epoches on a TITAN X (total time ~ 5 hours).
As a proof-of-concept, results were so-so; I think they are better than any traditional techniques, but could still be improved a lot.
Top: Original binarized image. Middle: Sketchify applied. Bottom: Originals (ground truth)
Implementation branched from here. Updated to Python 3.5 & tensorflow-gpu 1.2.1 syntax.
Deep Learning/Image processing: Tensorflow, OpenCV (cv2), PIL, numpy, matplotlib
Data collection: praw, requests, BeautifulSoup
[1] Deepcolor: Outline Colorization through Tandem Adversarial Networks.
[2] U-Net: Convolutional Networks for Biomedical Image Segmentation