融合 RGB 和 CASSI 的 MLP 架构,用于计算光谱成像

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Computer Vision and Image Understanding Pub Date : 2024-10-25 DOI:10.1016/j.cviu.2024.104214
Zeyu Cai , Ru Hong , Xun Lin , Jiming Yang , YouLiang Ni , Zhen Liu , Chengqian Jin , Feipeng Da
{"title":"融合 RGB 和 CASSI 的 MLP 架构,用于计算光谱成像","authors":"Zeyu Cai ,&nbsp;Ru Hong ,&nbsp;Xun Lin ,&nbsp;Jiming Yang ,&nbsp;YouLiang Ni ,&nbsp;Zhen Liu ,&nbsp;Chengqian Jin ,&nbsp;Feipeng Da","doi":"10.1016/j.cviu.2024.104214","DOIUrl":null,"url":null,"abstract":"<div><div>The coded Aperture Snapshot Spectral Imaging (CASSI) system offers significant advantages in dynamically acquiring hyper-spectral images compared to traditional measurement methods. However, it faces the following challenges: (1) Traditional masks rely on random patterns or analytical design, limiting CASSI’s performance improvement. (2) Existing CASSI reconstruction algorithms do not fully utilize RGB information. (3) High-quality reconstruction algorithms are often slow and limited to offline scene reconstruction. To address these issues, this paper proposes a new MLP architecture, Spectral–Spatial MLP (SSMLP), which replaces the transformer structure with a network using CASSI measurements and RGB as multimodal inputs. This maintains reconstruction quality while significantly improving reconstruction speed. Additionally, we constructed a teacher-student network (SSMLP with a teacher, SSMLP-WT) to transfer the knowledge learned from a large model to a smaller network, further enhancing the smaller network’s accuracy. Extensive experiments show that SSMLP matches the performance of transformer-based structures in spectral image reconstruction while improving inference speed by at least 50%. The reconstruction quality of SSMLP-WT is further improved by knowledge transfer without changing the network, and the teacher boosts the performance by 0.92 dB (44.73 dB vs. 43.81 dB).</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A MLP architecture fusing RGB and CASSI for computational spectral imaging\",\"authors\":\"Zeyu Cai ,&nbsp;Ru Hong ,&nbsp;Xun Lin ,&nbsp;Jiming Yang ,&nbsp;YouLiang Ni ,&nbsp;Zhen Liu ,&nbsp;Chengqian Jin ,&nbsp;Feipeng Da\",\"doi\":\"10.1016/j.cviu.2024.104214\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The coded Aperture Snapshot Spectral Imaging (CASSI) system offers significant advantages in dynamically acquiring hyper-spectral images compared to traditional measurement methods. However, it faces the following challenges: (1) Traditional masks rely on random patterns or analytical design, limiting CASSI’s performance improvement. (2) Existing CASSI reconstruction algorithms do not fully utilize RGB information. (3) High-quality reconstruction algorithms are often slow and limited to offline scene reconstruction. To address these issues, this paper proposes a new MLP architecture, Spectral–Spatial MLP (SSMLP), which replaces the transformer structure with a network using CASSI measurements and RGB as multimodal inputs. This maintains reconstruction quality while significantly improving reconstruction speed. Additionally, we constructed a teacher-student network (SSMLP with a teacher, SSMLP-WT) to transfer the knowledge learned from a large model to a smaller network, further enhancing the smaller network’s accuracy. Extensive experiments show that SSMLP matches the performance of transformer-based structures in spectral image reconstruction while improving inference speed by at least 50%. The reconstruction quality of SSMLP-WT is further improved by knowledge transfer without changing the network, and the teacher boosts the performance by 0.92 dB (44.73 dB vs. 43.81 dB).</div></div>\",\"PeriodicalId\":50633,\"journal\":{\"name\":\"Computer Vision and Image Understanding\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-10-25\",\"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/S1077314224002959\",\"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/S1077314224002959","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}
引用次数: 0

摘要

与传统测量方法相比,编码孔径快照光谱成像(CASSI)系统在动态获取高光谱图像方面具有显著优势。然而,它也面临着以下挑战:(1)传统的掩膜依赖于随机模式或分析设计,限制了 CASSI 性能的提高。(2) 现有的 CASSI 重建算法没有充分利用 RGB 信息。(3) 高质量的重建算法通常速度较慢,且仅限于离线场景重建。为了解决这些问题,本文提出了一种新的 MLP 架构--光谱空间 MLP (SSMLP),它利用 CASSI 测量和 RGB 作为多模态输入,用一个网络取代了变压器结构。这既保持了重建质量,又大大提高了重建速度。此外,我们还构建了一个师生网络(SSMLP with a teacher,SSMLP-WT),将从大型模型中学到的知识转移到小型网络中,进一步提高了小型网络的准确性。大量实验表明,SSMLP 在光谱图像重建方面的性能与基于变压器的结构相当,同时推理速度至少提高了 50%。在不改变网络的情况下,通过知识转移,SSMLP-WT 的重建质量得到了进一步提高,教师将其性能提高了 0.92 dB(44.73 dB 对 43.81 dB)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
A MLP architecture fusing RGB and CASSI for computational spectral imaging
The coded Aperture Snapshot Spectral Imaging (CASSI) system offers significant advantages in dynamically acquiring hyper-spectral images compared to traditional measurement methods. However, it faces the following challenges: (1) Traditional masks rely on random patterns or analytical design, limiting CASSI’s performance improvement. (2) Existing CASSI reconstruction algorithms do not fully utilize RGB information. (3) High-quality reconstruction algorithms are often slow and limited to offline scene reconstruction. To address these issues, this paper proposes a new MLP architecture, Spectral–Spatial MLP (SSMLP), which replaces the transformer structure with a network using CASSI measurements and RGB as multimodal inputs. This maintains reconstruction quality while significantly improving reconstruction speed. Additionally, we constructed a teacher-student network (SSMLP with a teacher, SSMLP-WT) to transfer the knowledge learned from a large model to a smaller network, further enhancing the smaller network’s accuracy. Extensive experiments show that SSMLP matches the performance of transformer-based structures in spectral image reconstruction while improving inference speed by at least 50%. The reconstruction quality of SSMLP-WT is further improved by knowledge transfer without changing the network, and the teacher boosts the performance by 0.92 dB (44.73 dB vs. 43.81 dB).
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Computer Vision and Image Understanding
Computer Vision and Image Understanding 工程技术-工程:电子与电气
CiteScore
7.80
自引率
4.40%
发文量
112
审稿时长
79 days
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
期刊最新文献
Diffusion Models for Counterfactual Explanations Image compressive sensing reconstruction via nonlocal low-rank residual-based ADMM framework A MLP architecture fusing RGB and CASSI for computational spectral imaging A GCN and Transformer complementary network for skeleton-based action recognition Invisible backdoor attack with attention and steganography
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1