Pulse Coupled Neural Network Based Anisotropic Diffusion Method For 1F Noise Reduction

• doi: 10.1109/BICTA.2009.5338166
• title: Pulse Coupled Neural Network based anisotropic diffusion method for 1/f noise reduction
• publisher: IEEE
• isbn: 978-1-4244-3867-9
• issn:
• partnum: 09EX2762
• rank: 3333
• access_type: LOCKED
• content_type: Conferences
• abstract: The effectiveness of anisotropic diffusion based denoising methods on both noise suppression and edge preservation have been demonstrated in many previous researches. However, the appearance of pulse noise liked spots in the denoised images due to a few high level noises in the noisy images becomes one of its limitations. This paper presents a pulse coupled neural network (PCNN) based anisotropic diffusion method to solve this problem at the same time of 1/f noise reduction in the pinned-type CMOS image sensors (CIS). Different from the traditional methods, pixels are respectively pre-processed by a median filter and a Lee filter according to the time matrix of the PCNN. Experimental results reveal that the pulse noise liked spots are eliminated by the proposed method. And finally, conclusions on the better performance on both 1/f noise reduction and edge and detail preservation are carried out compared with previous de-noising methods, i.e., median filter, Wiener filter, Lee filter, and traditional anisotropic diffusion based filter. Furthermore, the results will be applicable to the CIS manufacturing and also contribute denoising to still camera and video camera.
• article_number: 5338166
• pdf_url: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5338166
• html_url: https://ieeexplore.ieee.org/document/5338166/
• abstract_url: https://ieeexplore.ieee.org/document/5338166/
• publication_title: 2009 Fourth International on Conference on Bio-Inspired Computing
• conference_location: Beijing, China
• conference_dates: 16-19 Oct. 2009
• publication_number: 5325022
• is_number: 5338059
• publication_year: 2009
• publication_date: 16-19 Oct. 2009
• start_page: 1
• end_page: 6
• citing_paper_count: 4
• citing_patent_count: 0
• download_count: 137
• insert_date: 20091117
• index_terms:
  • ieee_terms:
    • Neural networks
    • Anisotropic magnetoresistance
    • Noise reduction
    • Semiconductor device noise
    • Noise level
    • Wiener filter
    • Computational Intelligence Society
    • Additive noise
    • Gaussian noise
    • CMOS image sensors
  • dynamic_index_terms:
    • Denoising
    • Noise Reduction
    • Anisotropic Diffusion
    • Coupling Network
    • Pulse-coupled Neural Network
    • Anisotropic Diffusion Method
    • Video Camera
    • High Noise
    • Image Sensor
    • Camera Sensor
    • Image Noise
    • Median Filter
    • High Noise Levels
    • Noise Suppression
    • Active Noise Control
    • Noise-canceling
    • Noisy Images
    • Spots In Images
    • Wiener Filter
    • Denoising Methods
    • Detail Preservation
    • Lee Filter
    • Edge Preservation
    • Image Processing
    • Noisy Pixels
    • Peak Signal-to-noise Ratio
    • PSNR
    • Image Edge
    • Noise Reduction Method
    • Output Neurons
    • Neighboring Neurons
    • Additive Noise
    • Additive White Gaussian Noise
    • Synaptic Weights
    • Input Image
• isbn_formats:
  • format: CD, value: 978-1-4244-3867-9, isbnType: New-2005
  • format: Print ISBN, value: 978-1-4244-3866-2, isbnType: New-2005
• authors:
  • Author Name: Deng Zhang
    Affiliation: Graduate school of Information, Production and Systems, Waseda University, Kitakyushu, Japan
    Author URL: https://ieeexplore.ieee.org/author/37577841100
    ID: 37577841100
    Order: 1
    Author Affiliations:
    • Graduate school of Information, Production and Systems, Waseda University, Kitakyushu, Japan
  • Author Name: Toshi Hiro Nishimura
    Affiliation: Graduate school of Information, Production and Systems, Waseda University, Kitakyushu, Japan
    Author URL: https://ieeexplore.ieee.org/author/37287475800
    ID: 37287475800
    Order: 2
    Author Affiliations:
    • Graduate school of Information, Production and Systems, Waseda University, Kitakyushu, Japan

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: Not specified

Supporting Organizations

• supported_by: Not specified

Manuscript Details

• publication_date: 16-19 Oct. 2009

Relevancy Score

• score: 10
• missing_fields:
  • resolution
  • dynamic_range
  • pixel_size
  • dark_current
  • focal_length
  • aperture
  • field_of_view
  • distortion
  • frame_rate
  • sensitivity
  • shutter_speed
  • power_consumption
  • noise
  • cell_imaging
  • benefits
  • supported_by

Processed JSON Filename

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