{"title":"单图像超分辨率","authors":"Yu Song","doi":"10.61366/2576-2176.1067","DOIUrl":null,"url":null,"abstract":"Super-Resolution (SR) of a single image is a classic problem in computer vision. The goal of image super-resolution is to produce a high-resolution image from a low-resolution image. This paper presents a popular model, super-resolution convolutional neural network (SRCNN), to solve this problem. This paper also examines an improvement to SRCNN using a methodology known as generative adversarial network (GAN) which is better at adding texture details to the high resolution output.","PeriodicalId":113813,"journal":{"name":"Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal","volume":"94 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Single Image Super-Resolution\",\"authors\":\"Yu Song\",\"doi\":\"10.61366/2576-2176.1067\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Super-Resolution (SR) of a single image is a classic problem in computer vision. The goal of image super-resolution is to produce a high-resolution image from a low-resolution image. This paper presents a popular model, super-resolution convolutional neural network (SRCNN), to solve this problem. This paper also examines an improvement to SRCNN using a methodology known as generative adversarial network (GAN) which is better at adding texture details to the high resolution output.\",\"PeriodicalId\":113813,\"journal\":{\"name\":\"Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal\",\"volume\":\"94 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-02-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.61366/2576-2176.1067\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.61366/2576-2176.1067","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Super-Resolution (SR) of a single image is a classic problem in computer vision. The goal of image super-resolution is to produce a high-resolution image from a low-resolution image. This paper presents a popular model, super-resolution convolutional neural network (SRCNN), to solve this problem. This paper also examines an improvement to SRCNN using a methodology known as generative adversarial network (GAN) which is better at adding texture details to the high resolution output.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
