Joint Deblurring And Demosaicing Of Poissonian Bayerdata Based On Local Adaptivity

• doi:
• title: Joint deblurring and demosaicing of Poissonian Bayer-data based on local adaptivity
• publisher: IEEE
• isbn:
• issn: 2219-5491
• rank: 2842
• access_type: LOCKED
• content_type: Conferences
• abstract: We present a novel technique for joint deblurring and demosaicing of noisy Poissonian Bayer data (e.g., data acquired by a digital CMOS or CCD imaging sensor). The technique incorporates the regularized inverse and the Wiener inverse with adaptive filtering based on the concept of cross-color local polynomial approximation (LPA) and intersection of confidence intervals (ICI). The directional filters designed by LPA utilize simultaneously the green, red, and blue color components. This is achieved by a linear combination of complementary-supported smoothing and derivative kernels designed for the Bayer data grid. The ICI rule is used for data-adaptive selection of the length of the designed cross-color directional filter. Simulation experiments demonstrate the efficiency of the proposed technique with respect to the conventional approach where deconvolution and demosaicing are computed independently.
• article_number: 7080598
• pdf_url: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7080598
• html_url: https://ieeexplore.ieee.org/document/7080598/
• abstract_url: https://ieeexplore.ieee.org/document/7080598/
• publication_title: 2008 16th European Signal Processing Conference
• conference_location: Lausanne, Switzerland
• conference_dates: 25-29 Aug. 2008
• publication_number: 7080219
• is_number: 7080220
• publication_year: 2008
• publication_date: 25-29 Aug. 2008
• start_page: 1
• end_page: 5
• citing_paper_count: 0
• citing_patent_count: 0
• download_count: 87
• insert_date: 20150406
• index_terms:
  • ieee_terms:
    • Image color analysis
    • Kernel
    • Noise
    • Interpolation
    • Convolution
    • Noise measurement
    • Joints
  • dynamic_index_terms:
    • Poisson Distribution
    • Poissonian
    • Digital Images
    • Deconvolution
    • Noisy Data
    • Color Space
    • Color Components
    • Digital Sensor
    • Variance Estimates
    • Loss Of Generality
    • Frequency Domain
    • Color Images
    • Convolution Operation
    • Regularization Parameter
    • Downscaling
    • Point Spread Function
    • Blurred Images
    • Independent Steps
    • Up-sampling Operation
    • Full-size Images
    • True-color Image
    • Down-sampling Operation
• authors:
  • Author Name: Dmytro Paliy
    Affiliation: Department of Signal Processing, Tampere University of Technology, Tampere, Finland
    Author URL: https://ieeexplore.ieee.org/author/37085687678
    ID: 37085687678
    Order: 1
    Author Affiliations:
    • Department of Signal Processing, Tampere University of Technology, Tampere, Finland
  • Author Name: Alessandro Foi
    Affiliation: Department of Signal Processing, Tampere University of Technology, Tampere, Finland
    Author URL: https://ieeexplore.ieee.org/author/37294575400
    ID: 37294575400
    Order: 2
    Author Affiliations:
    • Department of Signal Processing, Tampere University of Technology, Tampere, Finland
  • Author Name: Radu Bilcu
    Affiliation: Nokia Research Center, Tampere, Finland
    Author URL: https://ieeexplore.ieee.org/author/37285066200
    ID: 37285066200
    Order: 3
    Author Affiliations:
    • Nokia Research Center, Tampere, Finland
  • Author Name: Vladimir Katkovnik
    Affiliation: Department of Signal Processing, Tampere University of Technology, Tampere, Finland
    Author URL: https://ieeexplore.ieee.org/author/37284996400
    ID: 37284996400
    Order: 4
    Author Affiliations:
    • Department of Signal Processing, Tampere University of Technology, Tampere, Finland
  • Author Name: Karen Egiazarian
    Affiliation: Department of Signal Processing, Tampere University of Technology, Tampere, Finland
    Author URL: https://ieeexplore.ieee.org/author/37267440500
    ID: 37267440500
    Order: 5
    Author Affiliations:
    • Department of Signal Processing, Tampere University of Technology, Tampere, Finland

Image Sensor

• sensor_type: CMOS
• resolution: not specified
• 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

Applications & Benefits

• cell_imaging: not specified
• benefits: CMOS image sensors enable digital imaging in smartphones, medical devices, and automotive systems.

Supporting Organizations

• supported_by: Finnish Funding Agency for Technology and Innovation (Tekes), Academy of Finland, Nokia Research Center.

Manuscript Details

• publication_date: 25-29 Aug. 2008

Relevancy Score

• score: 8
• missing_fields:
  • resolution
  • dynamic_range
  • pixel_size
  • dark_current
  • focal_length
  • aperture
  • field_of_view
  • distortion
  • frame_rate
  • signal_to_noise_ratio
  • sensitivity
  • shutter_speed
  • power_consumption
  • noise

Processed JSON Filename

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