Zhisheng Cui , Yibing Yao , Shilong Li , Yongcan Zhao , Ming Xin
{"title":"用于图像超分辨率的轻量级哈希定向全局感知和自校准多尺度融合网络","authors":"Zhisheng Cui , Yibing Yao , Shilong Li , Yongcan Zhao , Ming Xin","doi":"10.1016/j.imavis.2024.105255","DOIUrl":null,"url":null,"abstract":"<div><p>In recent years, with the increase in the depth and width of convolutional neural networks, single image super-resolution (SISR) algorithms have made significant breakthroughs in objective quantitative metrics and subjective visual quality. However, these operations have inevitably caused model inference time to surge. In order to find a balance between model speed and accuracy, we propose a lightweight hash-directed global perception and self-calibrated multiscale fusion network for image Super-Resolution (HSNet) in this paper. The HSNet makes the following two main improvements: first, the Hash-Directed Global Perception module (HDGP) designed in this paper is able to capture the dependencies between features in a global perspective by using the hash encoding to direct the attention mechanism. Second, the Self-Calibrated Multiscale Fusion module (SCMF) proposed in this paper has two independent task branches: the upper branch of the SCMF utilizes the feature fusion module to capture multiscale contextual information, while the lower branch focuses on local details through a small convolutional kernel. These two branches are fused with each other to effectively enhance the network's multiscale understanding capability. Extensive experimental results demonstrate the remarkable superiority of our approach over other state-of-the-art methods in both subjective visual effects and objective evaluation metrics, including PSNR, SSIM, and computational complexity.</p></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"151 ","pages":"Article 105255"},"PeriodicalIF":4.2000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A lightweight hash-directed global perception and self-calibrated multiscale fusion network for image super-resolution\",\"authors\":\"Zhisheng Cui , Yibing Yao , Shilong Li , Yongcan Zhao , Ming Xin\",\"doi\":\"10.1016/j.imavis.2024.105255\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In recent years, with the increase in the depth and width of convolutional neural networks, single image super-resolution (SISR) algorithms have made significant breakthroughs in objective quantitative metrics and subjective visual quality. However, these operations have inevitably caused model inference time to surge. In order to find a balance between model speed and accuracy, we propose a lightweight hash-directed global perception and self-calibrated multiscale fusion network for image Super-Resolution (HSNet) in this paper. The HSNet makes the following two main improvements: first, the Hash-Directed Global Perception module (HDGP) designed in this paper is able to capture the dependencies between features in a global perspective by using the hash encoding to direct the attention mechanism. Second, the Self-Calibrated Multiscale Fusion module (SCMF) proposed in this paper has two independent task branches: the upper branch of the SCMF utilizes the feature fusion module to capture multiscale contextual information, while the lower branch focuses on local details through a small convolutional kernel. These two branches are fused with each other to effectively enhance the network's multiscale understanding capability. Extensive experimental results demonstrate the remarkable superiority of our approach over other state-of-the-art methods in both subjective visual effects and objective evaluation metrics, including PSNR, SSIM, and computational complexity.</p></div>\",\"PeriodicalId\":50374,\"journal\":{\"name\":\"Image and Vision Computing\",\"volume\":\"151 \",\"pages\":\"Article 105255\"},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2024-09-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Image and Vision Computing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0262885624003603\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Image and Vision Computing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0262885624003603","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
A lightweight hash-directed global perception and self-calibrated multiscale fusion network for image super-resolution
In recent years, with the increase in the depth and width of convolutional neural networks, single image super-resolution (SISR) algorithms have made significant breakthroughs in objective quantitative metrics and subjective visual quality. However, these operations have inevitably caused model inference time to surge. In order to find a balance between model speed and accuracy, we propose a lightweight hash-directed global perception and self-calibrated multiscale fusion network for image Super-Resolution (HSNet) in this paper. The HSNet makes the following two main improvements: first, the Hash-Directed Global Perception module (HDGP) designed in this paper is able to capture the dependencies between features in a global perspective by using the hash encoding to direct the attention mechanism. Second, the Self-Calibrated Multiscale Fusion module (SCMF) proposed in this paper has two independent task branches: the upper branch of the SCMF utilizes the feature fusion module to capture multiscale contextual information, while the lower branch focuses on local details through a small convolutional kernel. These two branches are fused with each other to effectively enhance the network's multiscale understanding capability. Extensive experimental results demonstrate the remarkable superiority of our approach over other state-of-the-art methods in both subjective visual effects and objective evaluation metrics, including PSNR, SSIM, and computational complexity.
期刊介绍:
Image and Vision Computing has as a primary aim the provision of an effective medium of interchange for the results of high quality theoretical and applied research fundamental to all aspects of image interpretation and computer vision. The journal publishes work that proposes new image interpretation and computer vision methodology or addresses the application of such methods to real world scenes. It seeks to strengthen a deeper understanding in the discipline by encouraging the quantitative comparison and performance evaluation of the proposed methodology. The coverage includes: image interpretation, scene modelling, object recognition and tracking, shape analysis, monitoring and surveillance, active vision and robotic systems, SLAM, biologically-inspired computer vision, motion analysis, stereo vision, document image understanding, character and handwritten text recognition, face and gesture recognition, biometrics, vision-based human-computer interaction, human activity and behavior understanding, data fusion from multiple sensor inputs, image databases.