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Found 5 papers, 1 papers with code
TuiGAN: Learning Versatile Image-to-Image Translation with Two Unpaired Images
With TuiGAN, an image is translated in a coarse-to-fine manner where the generated image is gradually refined from global structures to local details.
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020.
FAN: Frequency Aggregation Network for Real Image Super-resolution
Specifically, we extract different frequencies of the LR image and pass them to a channel attention-grouped residual dense network (CA-GRDB) individually to output corresponding feature maps.
Learning Omni-frequency Region-adaptive Representations for Real Image Super-Resolution
Traditional single image super-resolution (SISR) methods that focus on solving single and uniform degradation (i. e., bicubic down-sampling), typically suffer from poor performance when applied into real-world low-resolution (LR) images due to the complicated realistic degradations.
Image-to-Image Translation: Methods and Applications
Image-to-image translation (I2I) aims to transfer images from a source domain to a target domain while preserving the content representations.
