Onarray Compressive Acquisition In Cmos Image Sensors Using Accumulated Spatial Gradients

• doi: 10.1109/TCSVT.2020.2989359
• title: On-Array Compressive Acquisition in CMOS Image Sensors Using Accumulated Spatial Gradients
• publisher: IEEE
• isbn:
• issn: 1558-2205
• rank: 3175
• access_type: LOCKED
• content_type: Journals
• abstract: A compressive acquisition technique for on-array image compression is proposed in this paper. It capitalizes on representation ability of accumulated spatial gradients of the acquired scene. The local variations inferred from strength of the accumulated gradients are used as cues to vary number of samples read through the image sensor readout. Such sampling enables the reconstruction using traditional interpolation techniques with desired quality. The proposed method is first verified using MATLAB simulations, where on an average, a compression of 87% is achieved, for a threshold of 40 intensity levels. The images are reconstructed using nearest neighbour interpolation (NNI) method which results in a mean peak signal to noise ratio (PSNR) value of 29.09 dB. The reconstructed images are further enhanced using deep convolutional neural network, which improves the PSNR to 32.46 dB. The biggest advantage of the proposed technique is low-complex hardware design. As a proof of concept, a hardware implementation of the technique is performed using discrete components. Pixel intensity values of standard images are converted into analog voltages using a data acquisition system and mapped in the input voltage range of 1.5 V −5.5 V. For a threshold of 3.8 V, the compression of 81% - 83% is observed for the considered images. The proposed technique is simple and effective, and is suitable for low-power complementary metal oxide semiconductor (CMOS) image sensors.
• article_number: 9075293
• pdf_url: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9075293
• html_url: https://ieeexplore.ieee.org/document/9075293/
• abstract_url: https://ieeexplore.ieee.org/document/9075293/
• publication_title: IEEE Transactions on Circuits and Systems for Video Technology
• conference_location:
• conference_dates:
• publication_number: 76
• is_number: 9346111
• publication_year: 2021
• publication_date: Feb. 2021
• start_page: 523
• end_page: 532
• citing_paper_count: 7
• citing_patent_count: 0
• download_count: 807
• insert_date: 20200421
• index_terms:
  • ieee_terms:
    • Image coding
    • Image sensors
    • Silicon
    • Image reconstruction
    • Interpolation
    • Discrete cosine transforms
    • Hardware
  • author_terms:
    • On-array compression
    • CMOS image sensors
    • nearest neighbour interpolation
    • deep convolution neural network
  • dynamic_index_terms:
    • Image Sensor
    • Spatial Gradients
    • Compressed Acquisition
    • Convolutional Neural Network
    • Proof Of Concept
    • Deep Convolutional Neural Network
    • Data Acquisition System
    • Image Compression
    • Discrete Components
    • Nearest Neighbor Interpolation
    • Analog Voltage
    • Cut-off Value
    • Simulation Results
    • Image Processing
    • Source Code
    • Infrared Imaging
    • Image Reconstruction
    • Discrete Cosine Transform
    • Pixel Array
    • Integrated Circuit
    • Plate Capacitor
    • Bilinear Interpolation
    • Output Pixel
    • Adaptive Sampling
    • Cadence
    • Reconstruction Quality
    • Analog-to-digital Converter
• authors:
  • Author Name: Amandeep Kaur
    Affiliation: Department of Electrical Engineering, IIT Jodhpur, Jodhpur, India
    Author URL: https://ieeexplore.ieee.org/author/37085856535
    ID: 37085856535
    Order: 1
    Author Affiliations:
    • Department of Electrical Engineering, IIT Jodhpur, Jodhpur, India
  • Author Name: Deepak Mishra
    Affiliation: Department of Computer Science and Engineering, IIT Jodhpur, Jodhpur, India
    Author URL: https://ieeexplore.ieee.org/author/37085433827
    ID: 37085433827
    Order: 2
    Author Affiliations:
    • Department of Computer Science and Engineering, IIT Jodhpur, Jodhpur, India
  • Author Name: K. M. Amogh
    Affiliation: Electronics and Communication Department, PES University, Bengaluru, India
    Author URL: https://ieeexplore.ieee.org/author/37089939210
    ID: 37089939210
    Order: 3
    Author Affiliations:
    • Electronics and Communication Department, PES University, Bengaluru, India
  • Author Name: Mukul Sarkar
    Affiliation: Department of Electrical Engineering, IIT Delhi, New Delhi, India
    Author URL: https://ieeexplore.ieee.org/author/37394227700
    ID: 37394227700
    Order: 4
    Author Affiliations:
    • Department of Electrical Engineering, IIT Delhi, New Delhi, India

Image Sensor

• sensor_type: CMOS
• resolution: 512x512
• dynamic_range: Not specified
• 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

• frame_rate: 120 fps
• signal_to_noise_ratio: 29.09 dB (before CNN enhancement), 32.46 dB (after CNN enhancement)
• power_consumption: 78 μW
• noise: 3.91 μV/√Hz (integrator circuit noise), 3.97 μV/√Hz (total integrated noise)

Applications & Benefits

• cell_imaging: Used in biomedical instruments and digital photography.
• benefits: Low-complex hardware design, power-saving through on-array compression.

Supporting Organizations

• supported_by: IIT Jodhpur, PES University, IIT Delhi

Manuscript Details

• publication_date: Feb. 2021

Relevancy Score

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

Processed JSON Filename

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