반응형 컴퓨터비전(CV)6 srgan 코드 분석 # https://github.com/eriklindernoren/PyTorch-GAN/blob/master/implementations/srgan/models.py # models.py import torch.nn as nn import torch.nn.functional as F import torch from torchvision.models import vgg19 import math class FeatureExtractor(nn.Module): def __init__(self): super(FeatureExtractor, self).__init__() vgg19_model = vgg19(pretrained=True) self.feature_extractor = nn.Sequential(*list.. 2022. 7. 24. [논문 리뷰] Real-ESRGAN https://openaccess.thecvf.com/content/ICCV2021W/AIM/papers/Wang_Real-ESRGAN_Training_Real-World_Blind_Super-Resolution_With_Pure_Synthetic_Data_ICCVW_2021_paper.pdf 0. Abstract 지금까지 SR problem에서 training을 진행하기 위해 unknown and complex degradations을 통해 LR image를 만들어왔다. 하지만 이러한 인공적인 degradation들은 real world degraded image를 여전히 표현하지 못하고 있다. 이에 1. high-order degradation modeling process를 통해 complex re.. 2022. 7. 6. [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 2 다음 반응형
