![]() Textures from heavily downsampled images on public benchmarks. Our deep residual network is able to recover photo-realistic In addition, we useĪ content loss motivated by perceptual similarity instead of similarity in Super-resolved images and original photo-realistic images. Using a discriminator network that is trained to differentiate between the ![]() The adversarial loss pushes our solution to the natural image manifold ![]() Perceptual loss function which consists of an adversarial loss and a content Fiji is an image processing package a 'batteries-included' distribution of ImageJ, bundling many plugins which facilitate scientific image analysis. Knowledge, it is the first framework capable of inferring photo-realistic Generative adversarial network (GAN) for image super-resolution (SR). Signal-to-noise ratios, but they are often lacking high-frequency details andĪre perceptually unsatisfying in the sense that they fail to match the fidelityĮxpected at the higher resolution. Recent work has largely focused on minimizing the Optimization-based super-resolution methods is principally driven by the choice Super-resolution using faster and deeper convolutional neural networks, oneĬentral problem remains largely unsolved: how do we recover the finer textureĭetails when we super-resolve at large upscaling factors? The behavior of The aim is to characterize the orientation and isotropy properties of a region of interest (ROI) in an image, based on the evaluation of the gradient structure tensor in a local neighborhood. Written by Daniel Sage at the Biomedical Image Group (BIG), EPFL, Switzerland. Download a PDF of the paper titled Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, by Christian Ledig and 10 other authors Download PDF Abstract: Despite the breakthroughs in accuracy and speed of single image ImageJ plugins for directional analysis in images.
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