{"title":"用于轻量级图像超分辨率的高效特征重用蒸馏网络","authors":"","doi":"10.1016/j.cviu.2024.104178","DOIUrl":null,"url":null,"abstract":"<div><div>In recent research, single-image super-resolution (SISR) using deep Convolutional Neural Networks (CNN) has seen significant advancements. While previous methods excelled at learning complex mappings between low-resolution (LR) and high-resolution (HR) images, they often required substantial computational and memory resources. We propose the Efficient Feature Reuse Distillation Network (EFRDN) to alleviate these challenges. EFRDN primarily comprises Asymmetric Convolutional Distillation Modules (ACDM), incorporating the Multiple Self-Calibrating Convolution (MSCC) units for spatial and channel feature extraction. It includes an Asymmetric Convolution Residual Block (ACRB) to enhance the skeleton information of the square convolution kernel and a Feature Fusion Lattice Block (FFLB) to convert low-order input signals into higher-order representations. Introducing a Transformer module for global features, we enhance feature reuse and gradient flow, improving model performance and efficiency. Extensive experimental results demonstrate that EFRDN outperforms existing methods in performance while conserving computing and memory resources.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An efficient feature reuse distillation network for lightweight image super-resolution\",\"authors\":\"\",\"doi\":\"10.1016/j.cviu.2024.104178\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>In recent research, single-image super-resolution (SISR) using deep Convolutional Neural Networks (CNN) has seen significant advancements. While previous methods excelled at learning complex mappings between low-resolution (LR) and high-resolution (HR) images, they often required substantial computational and memory resources. We propose the Efficient Feature Reuse Distillation Network (EFRDN) to alleviate these challenges. EFRDN primarily comprises Asymmetric Convolutional Distillation Modules (ACDM), incorporating the Multiple Self-Calibrating Convolution (MSCC) units for spatial and channel feature extraction. It includes an Asymmetric Convolution Residual Block (ACRB) to enhance the skeleton information of the square convolution kernel and a Feature Fusion Lattice Block (FFLB) to convert low-order input signals into higher-order representations. Introducing a Transformer module for global features, we enhance feature reuse and gradient flow, improving model performance and efficiency. Extensive experimental results demonstrate that EFRDN outperforms existing methods in performance while conserving computing and memory resources.</div></div>\",\"PeriodicalId\":50633,\"journal\":{\"name\":\"Computer Vision and Image Understanding\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-09-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Vision and Image Understanding\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1077314224002595\",\"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":"Computer Vision and Image Understanding","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1077314224002595","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}
An efficient feature reuse distillation network for lightweight image super-resolution
In recent research, single-image super-resolution (SISR) using deep Convolutional Neural Networks (CNN) has seen significant advancements. While previous methods excelled at learning complex mappings between low-resolution (LR) and high-resolution (HR) images, they often required substantial computational and memory resources. We propose the Efficient Feature Reuse Distillation Network (EFRDN) to alleviate these challenges. EFRDN primarily comprises Asymmetric Convolutional Distillation Modules (ACDM), incorporating the Multiple Self-Calibrating Convolution (MSCC) units for spatial and channel feature extraction. It includes an Asymmetric Convolution Residual Block (ACRB) to enhance the skeleton information of the square convolution kernel and a Feature Fusion Lattice Block (FFLB) to convert low-order input signals into higher-order representations. Introducing a Transformer module for global features, we enhance feature reuse and gradient flow, improving model performance and efficiency. Extensive experimental results demonstrate that EFRDN outperforms existing methods in performance while conserving computing and memory resources.
期刊介绍:
The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views.
Research Areas Include:
• Theory
• Early vision
• Data structures and representations
• Shape
• Range
• Motion
• Matching and recognition
• Architecture and languages
• Vision systems