Compressively Sensed Thermal Image Panorama with Enchanced Resolution

被引:0
|
作者
Malczewski, Krzysztof [1 ]
Buczkowski, Mateusz [1 ]
机构
[1] Poznan Univ Tech, Dept Elect & Telecommun, Poznan, Poland
来源
2014 INTERNATIONAL CONFERENCE ON SIGNALS AND ELECTRONIC SYSTEMS (ICSES) | 2014年
关键词
compressive sensing; people tracking; super-resolution; thermal imaging; motion analysis;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Thermal Imaging can be really valuable to military, other users of surveillance camera and physicians. Contrary to the usual sampling process where weavers want to sample at the Nyquist rate the Compressive Sensing provides a stricter sampling condition when the signal is known to be sparse or compressible. It turned out that the Nyquist-Shannon theorem is a sufficient condition but not a necessary condition for perfect reconstruction. The compressive sensing field of interest provides a stricter sampling condition when the signal is known to be sparse or compressible. This theorem still struggles with three main problems: sparse representation, measurement matrix and reconstruction algorithm. Unfortunately, thermal image camera resolution is considerably lower than of optical cameras, mostly only 160x120 or 320x240 pixels narrow view. The 360-degree infrared surveillance would be the clear choice. Day or night, the IR Revolution 360 panoramic infrared camera provides instantaneous, long range, wide area, high resolution thermal imaging. The method utilizes double sensor thermal imaging cameras. Moreover the temporal resolution has been enhanced. This paper goal is to keep evolving with compressed sensing and thermal imaging technology.
引用
收藏
页数:4
相关论文
共 50 条
  • [1] Compressively Sensed Image Recognition
    Degerli, Aysen
    Aslan, Sinem
    Yamac, Mehmet
    Sankur, Bulent
    Gabbouj, Moncef
    PROCEEDINGS OF THE 2018 7TH EUROPEAN WORKSHOP ON VISUAL INFORMATION PROCESSING (EUVIP), 2018,
  • [2] Image Resolution Enhancement of Highly Compressively Sensed CT/PET Signals
    Malczewski, Krzysztof
    ALGORITHMS, 2020, 13 (05)
  • [3] Interactive Image and Video Classification using Compressively Sensed Images
    Stubbs, Jaclynn J.
    Pattichis, Marios S.
    Birch, Gabriel C.
    2017 FIFTY-FIRST ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS, AND COMPUTERS, 2017, : 2038 - 2041
  • [4] Deep neural network classification in the compressively sensed spectral image domain
    Cohen, Nadav
    Shmilovich, Shauli
    Oiknine, Yaniv
    Stern, Adrian
    JOURNAL OF ELECTRONIC IMAGING, 2021, 30 (04)
  • [5] FORENSIC IDENTIFICATION OF COMPRESSIVELY SENSED IMAGES
    Chu, Xiaoyu
    Stamm, Matthew C.
    Lin, W. Sabrina
    Liu, K. J. Ray
    2012 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2012, : 1837 - 1840
  • [6] FORENSIC IDENTIFICATION OF COMPRESSIVELY SENSED SIGNALS
    Chu, Xiaoyu
    Stamm, Matthew C.
    Liu, K. J. Ray
    2012 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2012), 2012, : 257 - 260
  • [7] Optimization Methods of Compressively Sensed Image Reconstruction Based on Single-Pixel Imaging
    Wei, Ziran
    Zhang, Jianlin
    Xu, Zhiyong
    Liu, Yong
    APPLIED SCIENCES-BASEL, 2020, 10 (09):
  • [8] Signal Quality Assessment of Compressively Sensed Electrocardiogram
    Abdelazez, Mohamed
    Rajan, Sreeraman
    Chan, Adrian D. C.
    IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2022, 69 (11) : 3397 - 3406
  • [9] Radar target classification using compressively sensed features
    Jouny, Ismail
    SIGNAL PROCESSING, SENSOR/INFORMATION FUSION, AND TARGET RECOGNITION XXVI, 2017, 10200
  • [10] Privacy Assured Recovery of Compressively Sensed ECG Signals
    Zanddizari, Hadi
    Rajan, Sreeraman
    Zarrabi, Houman
    Rabah, Hassan
    IEEE ACCESS, 2022, 10 : 17122 - 17133