반응형 super resolution2 [논문 리뷰] Photo-Realistic Single Image Super-Resolution Using a Generative AdversarialNetwork(SRGAN) https://arxiv.org/abs/1609.04802 Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture details when we super-resolve at large arxiv.org 0. Abstract 이.. 2022. 6. 23. [논문 리뷰] Perceptual Losses for Real-Time Style Transfer and Super-Resolution https://arxiv.org/abs/1603.08155 Perceptual Losses for Real-Time Style Transfer and Super-Resolution We consider image transformation problems, where an input image is transformed into an output image. Recent methods for such problems typically train feed-forward convolutional neural networks using a \emph{per-pixel} loss between the output and ground-tru arxiv.org 0. abstract 기존 image transform.. 2022. 5. 9. 이전 1 다음 반응형
