Thesis Details
Zvyšování kvality videa pomocí konvolučních sítí
Convolutional neural networks (CNN) represent a state-of-the-art approach to non-trivial image processing tasks, including compression artifacts reduction and image super-resolution. As some research groups nowadays show, these networks can also be leveraged to perform such tasks on real-world video data, resulting in video spatial super-resolution and more. The main goal of this work is to determine whether these nets can be adjusted to perform temporal super-resolution of real-world video data. I utilize the aforementioned neural net architectures in this paper to do so. As I show, given that the input videos are of reasonable quality, these nets are capable of double-image interpolation up to a certain level, where the output image is usable for temporal upsampling. Although the presented results are promising, I encourage more research to be done on this topic.
Deep learning, machine learning, deep neural networks, convolutional neural networks, video, image quality, image restoration, enhancement, temporal resolution, frames per second, fps, video image interpolation
Češka Milan, doc. RNDr., Ph.D. (DITS FIT BUT), člen
Drábek Vladimír, doc. Ing., CSc. (DCSY FIT BUT), člen
Hliněná Dana, doc. RNDr., Ph.D. (DMAT FEEC BUT), člen
Rychlý Marek, RNDr., Ph.D. (DIFS FIT BUT), člen
@bachelorsthesis{FITBT19940, author = "David Sk\'{a}cel", type = "Bachelor's thesis", title = "Zvy\v{s}ov\'{a}n\'{i} kvality videa pomoc\'{i} konvolu\v{c}n\'{i}ch s\'{i}t\'{i}", school = "Brno University of Technology, Faculty of Information Technology", year = 2017, location = "Brno, CZ", language = "czech", url = "https://www.fit.vut.cz/study/thesis/19940/" }