{"url":"/method/srgan","slug":"srgan","name":"SRGAN","full_name":"SRGAN","full_name_withheld":false,"description_markdown":"**SRGAN** is a generative adversarial network for single image super-resolution. It uses a perceptual loss function which consists of an adversarial loss and a content loss. The adversarial loss pushes the solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images. In addition, the authors use a content loss motivated by perceptual similarity instead of similarity in pixel space. The actual networks - depicted in the Figure to the right - consist mainly of residual blocks for feature extraction.\r\n\r\nFormally we write the perceptual loss function as a weighted sum of a ([VGG](https://paperswithcode.com/method/vgg)) content loss $l^{SR}\\_{X}$ and an adversarial loss component $l^{SR}\\_{Gen}$:\r\n\r\n$$ l^{SR} = l^{SR}\\_{X} + 10^{-3}l^{SR}\\_{Gen} $$","description_state":"present","introduced_year":null,"introduced_by":{"title":null,"paper":null,"first_author":null,"n_authors":0,"url_abs":null,"archive_paper_url":null},"source":{"url":"http://arxiv.org/abs/1609.04802v5","title":"Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network","url_on_a_paper_host":true},"code_snippet_url":"https://github.com/lizhuoq/SRGAN.git","code_snippet_url_on_a_code_host":true,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Generative Adversarial 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