Quan Zou;Qiurong Yan;Qianling Dai;Ao Wang;Bo Yang;Yi Li;Jinwei Yan
{"title":"基于多重先验深度展开网络的单像素智能识别","authors":"Quan Zou;Qiurong Yan;Qianling Dai;Ao Wang;Bo Yang;Yi Li;Jinwei Yan","doi":"10.1109/JPHOT.2024.3420787","DOIUrl":null,"url":null,"abstract":"Single-pixel imaging (SPI), an imaging technique based on the theory of compressed sensing, is limited in real-time imaging and high-resolution images due to its relatively slow imaging speed. In recent years, deep unfolding network compressed sensing reconstruction algorithms based on deep learning have proven to be an effective solution for faster and higher quality image reconstruction. However, existing deep unfolding networks mainly rely on a single piece of a priori information and may ignore other intrinsic structures of the image. Therefore, in this paper, we propose a deep unfolding network (MPDU-Net) that incorporates multiple prior information. To effectively fuse multiple prior information, we propose three different fusion strategies in the deep reconstruction sub-network. An unbiased convolutional layer is used to simulate the sampling reconstruction process to achieve joint reconstruction for effective removal of block artifacts. The sampling matrix is input into the deep reconstruction sub-network as a learnable parameter to achieve joint optimization of sampling reconstruction. Simulation and practical experimental results show that the proposed network outperforms existing compressed sensing reconstruction algorithms based on deep unfolding networks.","PeriodicalId":13204,"journal":{"name":"IEEE Photonics Journal","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10577085","citationCount":"0","resultStr":"{\"title\":\"Single Pixel Imaging Based on Multiple Prior Deep Unfolding Network\",\"authors\":\"Quan Zou;Qiurong Yan;Qianling Dai;Ao Wang;Bo Yang;Yi Li;Jinwei Yan\",\"doi\":\"10.1109/JPHOT.2024.3420787\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Single-pixel imaging (SPI), an imaging technique based on the theory of compressed sensing, is limited in real-time imaging and high-resolution images due to its relatively slow imaging speed. In recent years, deep unfolding network compressed sensing reconstruction algorithms based on deep learning have proven to be an effective solution for faster and higher quality image reconstruction. However, existing deep unfolding networks mainly rely on a single piece of a priori information and may ignore other intrinsic structures of the image. Therefore, in this paper, we propose a deep unfolding network (MPDU-Net) that incorporates multiple prior information. To effectively fuse multiple prior information, we propose three different fusion strategies in the deep reconstruction sub-network. An unbiased convolutional layer is used to simulate the sampling reconstruction process to achieve joint reconstruction for effective removal of block artifacts. The sampling matrix is input into the deep reconstruction sub-network as a learnable parameter to achieve joint optimization of sampling reconstruction. Simulation and practical experimental results show that the proposed network outperforms existing compressed sensing reconstruction algorithms based on deep unfolding networks.\",\"PeriodicalId\":13204,\"journal\":{\"name\":\"IEEE Photonics Journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-06-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10577085\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Photonics Journal\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10577085/\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Photonics Journal","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10577085/","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Single Pixel Imaging Based on Multiple Prior Deep Unfolding Network
Single-pixel imaging (SPI), an imaging technique based on the theory of compressed sensing, is limited in real-time imaging and high-resolution images due to its relatively slow imaging speed. In recent years, deep unfolding network compressed sensing reconstruction algorithms based on deep learning have proven to be an effective solution for faster and higher quality image reconstruction. However, existing deep unfolding networks mainly rely on a single piece of a priori information and may ignore other intrinsic structures of the image. Therefore, in this paper, we propose a deep unfolding network (MPDU-Net) that incorporates multiple prior information. To effectively fuse multiple prior information, we propose three different fusion strategies in the deep reconstruction sub-network. An unbiased convolutional layer is used to simulate the sampling reconstruction process to achieve joint reconstruction for effective removal of block artifacts. The sampling matrix is input into the deep reconstruction sub-network as a learnable parameter to achieve joint optimization of sampling reconstruction. Simulation and practical experimental results show that the proposed network outperforms existing compressed sensing reconstruction algorithms based on deep unfolding networks.
期刊介绍:
Breakthroughs in the generation of light and in its control and utilization have given rise to the field of Photonics, a rapidly expanding area of science and technology with major technological and economic impact. Photonics integrates quantum electronics and optics to accelerate progress in the generation of novel photon sources and in their utilization in emerging applications at the micro and nano scales spanning from the far-infrared/THz to the x-ray region of the electromagnetic spectrum. IEEE Photonics Journal is an online-only journal dedicated to the rapid disclosure of top-quality peer-reviewed research at the forefront of all areas of photonics. Contributions addressing issues ranging from fundamental understanding to emerging technologies and applications are within the scope of the Journal. The Journal includes topics in: Photon sources from far infrared to X-rays, Photonics materials and engineered photonic structures, Integrated optics and optoelectronic, Ultrafast, attosecond, high field and short wavelength photonics, Biophotonics, including DNA photonics, Nanophotonics, Magnetophotonics, Fundamentals of light propagation and interaction; nonlinear effects, Optical data storage, Fiber optics and optical communications devices, systems, and technologies, Micro Opto Electro Mechanical Systems (MOEMS), Microwave photonics, Optical Sensors.