Towards improved video super resolution for real-world applications ; master's thesis in artificial intelligence
Year: 2018
Type: other
Abstract: Video or, Multi Frame, Super Resolution (VSR or, MFSR) techniques aim to generate high-resolution frame reconstructions of corresponding low-resolution ones. These techniques differ from the Single Image or, Frame, Super Resolution (SISR or, SFSR) ones in that they can additionally exploit the temporal nature of the input data. The advantage of having additional temporal information does not translate directly into an easier problem to solve. The challenge lies in the optimal extraction of this additional information. Current state-of-the-art methods are still using some variants of optical flow estimation plus warping for extraction and integration of temporal information but it is well known that during the warping process a lot of the high frequency information get lost. We investigated alternative architectures to alleviate orsuppress this problem but they only perform on par or slightly worst than current SOTA networks.
Source: TNO Repository
Author: R. Samperna
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3092499687