Improving quality of scanned visual content using convolutional neural networksToroman, Jovan (Avtor) Čehovin Zajc, Luka (Mentor) captured document quality enhancementsmartphone scannerconvolutional neural networksIn this work we approach the problem of image quality improvement for images of documents captured using smartphones. Our goal is to make content captured this way as similar as possible to the original groundtruth images. We make a twofold contribution, (1) we propose an innovative method for improving quality of documents using convolutional neural networks and (2) we create a training dataset containing images captured using smartphones under dierent external conditions (lighting, viewing angle). This dataset is captured under controlled external conditions using an acquisition setup developed for this purpose. In our work we use six dierent smartphones and one hundred groundtruth images. We build our work on two different existing convolutional neural network architectures, UNet and DPED network, using them as our starting point. Both models are adapted to our domain. We experiment with dierent hyperparameters for both networks, as well as with dierent forms of training. We evaluate results of the two variants of our method against each other and also against other baseline approaches (simple contrast enhancer and a function from Adobe Photoshop Express). We use standard image quality comparison metrics to objectively compare performance. From the results we can see that our work outperforms baseline approaches, especially in more dicult scenarios of uneven illumination. Finally, we discuss the results of our method and possible improvements.20202020-12-10 12:30:00Magistrsko delo/naloga122425sl