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During the training, A high-resolution image (HR) is downsampled to a low-resolution image (LR). A GAN generator upsamples LR images to super-resolution . For training SRResNet, we cropped 16 random 96 × 96 HR sub-images for each . Then we further trained three SRGANs (SRGAN-VGG, SRGAN-SDAE, . SRGAN uses the GAN to produce the high resolution images from the low . is used to distinguish the generated images from the HR images. Keras-SRGAN. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network implemented in Keras. For more about topic check . Results for ×4 super-resolution results on an image. L-R: Bicubic interpolation, SRGAN , GANReDL (our approach at r = 0.05), Original HR image. We see our . SRGAN and Wavelet-SRNet were trained on our training set using pairs of bilinearly downsampled - HR images. FSRNet provides only testing code (trained on . SRGANs are very useful to increase the resolution in images (or create . task of estimating a highresolution (HR) image from its low-resolution . gains in perceptual quality using SRGAN. . squared error (MSE) between the recovered HR image . adversarial network (SRGAN) for which we employ a. . resolution (LR) images and to process them with the Super Resolution Generative Adversarial Network (SRGAN) to create an HR version.