{"title":"使用超分辨率技术和自监督引导生成高分辨率面部表情图像","authors":"Tatsuya Hanano","doi":"10.18178/joig.11.3.302-308","DOIUrl":null,"url":null,"abstract":"The recent spread of smartphones and social networking services has increased the means of seeing images of human faces. Particularly, in the face image field, the generation of face images using facial expression transformation has already been realized using deep learning–based approaches. However, in the existing deep learning–based models, only low-resolution images can be generated due to limited computational resources. Consequently, the generated images are blurry or aliasing. To address this problem, we proposed a two-step method to enhance the resolution of the generated facial images by combining a super-resolution network following the generative model, which can be considered a serial model, in our previous work. We further proposed a parallel model that trains a generative adversarial network and a superresolution network through multitask learning. In this paper, we propose a new model that integrates self-supervised guidance encoders into the parallel model to further improve the accuracy of the generated results. Using the peak signalto- noise ratio as an evaluation index, image quality was improved by 0.25 dB for the male test data and 0.28 dB for the female test data compared with our previous multitaskbased parallel model.","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Generation of High-Resolution Facial Expression Images Using a Super-Resolution Technique and Self-Supervised Guidance\",\"authors\":\"Tatsuya Hanano\",\"doi\":\"10.18178/joig.11.3.302-308\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The recent spread of smartphones and social networking services has increased the means of seeing images of human faces. Particularly, in the face image field, the generation of face images using facial expression transformation has already been realized using deep learning–based approaches. However, in the existing deep learning–based models, only low-resolution images can be generated due to limited computational resources. Consequently, the generated images are blurry or aliasing. To address this problem, we proposed a two-step method to enhance the resolution of the generated facial images by combining a super-resolution network following the generative model, which can be considered a serial model, in our previous work. We further proposed a parallel model that trains a generative adversarial network and a superresolution network through multitask learning. In this paper, we propose a new model that integrates self-supervised guidance encoders into the parallel model to further improve the accuracy of the generated results. Using the peak signalto- noise ratio as an evaluation index, image quality was improved by 0.25 dB for the male test data and 0.28 dB for the female test data compared with our previous multitaskbased parallel model.\",\"PeriodicalId\":36336,\"journal\":{\"name\":\"中国图象图形学报\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"中国图象图形学报\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://doi.org/10.18178/joig.11.3.302-308\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"中国图象图形学报","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.18178/joig.11.3.302-308","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Computer Science","Score":null,"Total":0}
Generation of High-Resolution Facial Expression Images Using a Super-Resolution Technique and Self-Supervised Guidance
The recent spread of smartphones and social networking services has increased the means of seeing images of human faces. Particularly, in the face image field, the generation of face images using facial expression transformation has already been realized using deep learning–based approaches. However, in the existing deep learning–based models, only low-resolution images can be generated due to limited computational resources. Consequently, the generated images are blurry or aliasing. To address this problem, we proposed a two-step method to enhance the resolution of the generated facial images by combining a super-resolution network following the generative model, which can be considered a serial model, in our previous work. We further proposed a parallel model that trains a generative adversarial network and a superresolution network through multitask learning. In this paper, we propose a new model that integrates self-supervised guidance encoders into the parallel model to further improve the accuracy of the generated results. Using the peak signalto- noise ratio as an evaluation index, image quality was improved by 0.25 dB for the male test data and 0.28 dB for the female test data compared with our previous multitaskbased parallel model.