Generative Adversarial Networks and Perceptual Losses for Video Super-Resolution

doi:doi:10.57702/fq54p9n2

Generative Adversarial Networks and Perceptual Losses for Video Super-Resolution

Video super-resolution (VSR) has become one of the most critical problems in video processing. In the deep learning literature, recent works have shown the benefits of using adversarial-based and perceptual losses to improve the performance on various image restoration tasks; however, these have yet to be applied for video super-resolution.

BibTex:

@dataset{Alice_Lucas_and_Santiago_Lopez-Tapia_and_Rafael_Molina_and_Aggelos_K_Katsaggelos_2024,
    abstract = {Video super-resolution (VSR) has become one of the most critical problems in video processing. In the deep learning literature, recent works have shown the benefits of using adversarial-based and perceptual losses to improve the performance on various image restoration tasks; however, these have yet to be applied for video super-resolution.},
    author = {Alice Lucas and Santiago Lopez-Tapia and Rafael Molina and Aggelos K. Katsaggelos},
    doi = {10.57702/fq54p9n2},
    institution = {No Organization},
    keyword = {'Generative Adversarial Networks', 'Perceptual Losses', 'Video Super-Resolution'},
    month = {dec},
    publisher = {TIB},
    title = {Generative Adversarial Networks and Perceptual Losses for Video Super-Resolution},
    url = {https://service.tib.eu/ldmservice/dataset/generative-adversarial-networks-and-perceptual-losses-for-video-super-resolution},
    year = {2024}
}