Perception-oriented single image super-resolution using optimal objective estimation
Single-image super-resolution (SISR) networks trained with perceptual and adversarial
losses provide high-contrast outputs compared to those of networks trained with distortion …
losses provide high-contrast outputs compared to those of networks trained with distortion …
Perception-Oriented Single Image Super-Resolution using Optimal Objective Estimation
SH Park, YS Moon, NI Cho - arXiv e-prints, 2022 - ui.adsabs.harvard.edu
Single-image super-resolution (SISR) networks trained with perceptual and adversarial
losses provide high-contrast outputs compared to those of networks trained with distortion …
losses provide high-contrast outputs compared to those of networks trained with distortion …
Perception-Oriented Single Image Super-Resolution using Optimal Objective Estimation
SH Park, YS Moon, NI Cho - 2023 IEEE/CVF Conference on …, 2023 - computer.org
Single-image super-resolution (SISR) networks trained with perceptual and adversarial
losses provide high-contrast outputs compared to those of networks trained with distortion …
losses provide high-contrast outputs compared to those of networks trained with distortion …
Perception-Oriented Single Image Super-Resolution using Optimal Objective Estimation
SH Park, YS Moon, NI Cho - 2023 IEEE/CVF Conference on …, 2023 - ieeexplore.ieee.org
Single-image super-resolution (SISR) networks trained with perceptual and adversarial
losses provide high-contrast outputs compared to those of networks trained with distortion …
losses provide high-contrast outputs compared to those of networks trained with distortion …
Perception-Oriented Single Image Super-Resolution using Optimal Objective Estimation
SH Park, YS Moon, NI Cho - arXiv preprint arXiv:2211.13676, 2022 - arxiv.org
Single-image super-resolution (SISR) networks trained with perceptual and adversarial
losses provide high-contrast outputs compared to those of networks trained with distortion …
losses provide high-contrast outputs compared to those of networks trained with distortion …