Compressive Image Sensor Technique With Sparse Measurement Matrix

• doi: 10.1109/SOCC.2016.7905472
• title: Compressive image sensor technique with sparse measurement matrix
• publisher: IEEE
• isbn: 978-1-5090-1368-5
• issn: 2164-1706
• rank: 1987
• access_type: LOCKED
• content_type: Conferences
• abstract: This paper presents compressive image sensor techniques based on sparse measurement matrices. Existing compressive sensing (CS) CMOS image sensors use dense random measurement matrices, which face the challenges of excessive hardware overhead and large signal swing requirement. The sparse measurement matrices proposed in this paper dramatically simplify the circuit implementation and relax the signal swing requirement. The validity of the proposed sparse measurement matrices is justified and their performances are compared with results reported in literature as well as results obtained using dense random measurement matrices in our own study. Circuit techniques to implement CS image sensors based on sparse measurement matrices are discussed and a 250×250 Pixel image sensor with a compression rate of 250/62 is designed using a 0.13 μm CMOS technology. Circuit simulation shows that the image sensor can achieve a peak signal to noise ratio of 31.7dB.
• article_number: 7905472
• pdf_url: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7905472
• html_url: https://ieeexplore.ieee.org/document/7905472/
• abstract_url: https://ieeexplore.ieee.org/document/7905472/
• publication_title: 2016 29th IEEE International System-on-Chip Conference (SOCC)
• conference_location: Seattle, WA, USA
• conference_dates: 6-9 Sept. 2016
• publication_number: 7897353
• is_number: 7905397
• publication_year: 2016
• publication_date: 6-9 Sept. 2016
• start_page: 223
• end_page: 228
• citing_paper_count: 6
• citing_patent_count: 0
• download_count: 251
• insert_date: 20170424
• index_terms:
  • ieee_terms:
    • Biomedical measurement
    • Frequency measurement
    • Size measurement
    • Sparse matrices
    • Image coding
    • CMOS technology
    • Image resolution
  • author_terms:
    • image sensor
    • compressive sensing
  • dynamic_index_terms:
    • Sparse Matrix
    • Sparse Matrices
    • Dense Matrix
    • Image Sensor
    • Camera Sensor
    • Measurement Matrix
    • Measurement Matrices
    • Sparse Measurements
    • Compression Sensor
    • Sparse Measurement Matrix
    • Random Matrix
    • Random Matrices
    • CMOS Technology
    • Circuit Simulation
    • Circuit Implementation
    • Image Quality
    • Panel Of Fig
    • Frequency Components
    • Block Size
    • Analog-to-digital Converter
    • Analog-to-digital
    • Digital-to-analog Converter
    • Natural Images
    • Projection Matrix
    • Matrix Operations
    • Projection Matrices
    • State Machine
    • Finite-state
    • Finite State Machine
    • Low-frequency Range
    • Low-frequency Components
    • Restricted Isometry Property
    • Restricted Isometry Constant
    • MATLAB Simulation
    • Output Pixel
    • Random Bits
    • Group Of Pixels
    • Basis Pursuit
    • Coordinate Vector
    • High Frequency Components
    • Fourier Basis
    • Shift Register
• isbn_formats:
  • format: Print on Demand(PoD) ISBN, value: 978-1-5090-1368-5, isbnType: New-2005
  • format: USB ISBN, value: 978-1-5090-1366-1, isbnType: New-2005
  • format: Electronic ISBN, value: 978-1-5090-1367-8, isbnType: New-2005
• authors:
  • Author Name: Stefan Leitner
    Affiliation: Dept. of Electrical and Computer Engineering, Southern Illinois University, Carbondale, Illinois, USA
    Author URL: https://ieeexplore.ieee.org/author/37089134104
    ID: 37089134104
    Order: 1
    Author Affiliations:
    • Dept. of Electrical and Computer Engineering, Southern Illinois University, Carbondale, Illinois, USA
  • Author Name: Haibo Wang
    Affiliation: Dept. of Electrical and Computer Engineering, Southern Illinois University, Carbondale, Illinois, USA
    Author URL: https://ieeexplore.ieee.org/author/37292554000
    ID: 37292554000
    Order: 2
    Author Affiliations:
    • Dept. of Electrical and Computer Engineering, Southern Illinois University, Carbondale, Illinois, USA
  • Author Name: Spyros Tragoudas
    Affiliation: Dept. of Electrical and Computer Engineering, Southern Illinois University, Carbondale, Illinois, USA
    Author URL: https://ieeexplore.ieee.org/author/37265505900
    ID: 37265505900
    Order: 3
    Author Affiliations:
    • Dept. of Electrical and Computer Engineering, Southern Illinois University, Carbondale, Illinois, USA

Image Sensor

• sensor_type: CMOS
• resolution: 250x250
• dynamic_range: 31.7dB
• pixel_size: not specified
• dark_current: not specified

Optical Data

• focal_length: not specified
• aperture: not specified
• field_of_view: not specified
• distortion: not specified

Performance Metrics

• signal_to_noise_ratio: 31.7dB

Applications & Benefits

• cell_imaging: Applicable in handheld devices, wearable gadgets, and medical devices due to low power consumption and high resolution.
• benefits: Reduction in ADC operations and power consumption, compact design, and improvement in image quality.

Supporting Organizations

• supported_by: NSF liP 1535658; NSF I/UCRC for Embedded Systems at SIUC under grant NSF liP 1361847.

Manuscript Details

• publication_date: 6-9 Sept. 2016

Relevancy Score

• score: 10
• missing_fields:
  • fill factor
  • quantum efficiency
  • readout speed
  • temporal noise
  • fixed-pattern noise
  • microlenses
  • on-chip colour filters
  • global or rolling shutters
  • backside illumination

Processed JSON Filename

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