Noise Reduction Using Genetic Algorithm Based Pcnn Method

• doi: 10.1109/ICSMC.2010.5641902
• title: Noise reduction using genetic algorithm based PCNN method
• publisher: IEEE
• isbn: 978-1-4244-6587-3
• issn: 1062-922X
• partnum: 10CH38225
• rank: 3127
• access_type: LOCKED
• content_type: Conferences
• abstract: Pulse Coupled Neural Network (PCNN) has gained widely attention in image noise reduction as a non-linear filtering technique. Conventional PCNN-based methods usually are combined with median filter or step-by-step modifying algorithm. There are mainly two problems. In one hand, such filtering approaches blur the edge when smoothing an image. In the other hand, it is difficult to properly estimate the parameters of PCNN. Consequently, this paper presents an adaptive genetic algorithm based PCNN method (GA-PCNN) to restrain from additive white Gaussian noise (AWGN). Different from conventional PCNN-based methods, GA-PCNN utilizes an anisotropic diffusion filter to replace the median filter and optimizes the parameters of a simplified PCNN by means of adaptive genetic algorithm. Experimental results indicate that GA-PCNN has a better performance than the previous denoising techniques, i.e., median filter, Wiener filter, anisotropic diffusion filter, and the conventional PCNN based methods. Conclusions on the effectiveness of Gaussian noise reduction and edge preservation are carried out finally. Furthermore, the results will contribute de-noising in CMOS image sensors.
• article_number: 5641902
• pdf_url: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5641902
• html_url: https://ieeexplore.ieee.org/document/5641902/
• abstract_url: https://ieeexplore.ieee.org/document/5641902/
• publication_title: 2010 IEEE International Conference on Systems, Man and Cybernetics
• conference_location: Istanbul
• conference_dates: 10-13 Oct. 2010
• publication_number: 5629466
• is_number: 5641665
• publication_year: 2010
• publication_date: 10-13 Oct. 2010
• start_page: 2627
• end_page: 2633
• citing_paper_count: 10
• citing_patent_count: 0
• download_count: 537
• insert_date: 20101122
• index_terms:
  • ieee_terms:
    • Gallium
    • AWGN
    • Smoothing methods
  • author_terms:
    • Additive White Gaussian Noise
    • Genetic Algorithm
    • Noise Reduction
    • Pulse Coupled Neural Network
  • dynamic_index_terms:
    • Pulse-coupled Neural Network
    • Additive Noise
    • Additive White Gaussian Noise
    • Image Sensor
    • Camera Sensor
    • Median Filter
    • Anisotropic Diffusion
    • Wiener Filter
    • Reduce Image Noise
    • Edge Preservation
    • Low-pass
    • Low-pass Filter
    • Running Time
    • Iterative Algorithm
    • Fitness Function
    • Decision Variables
    • Spatial Domain
    • Thermal Noise
    • Current Noise
    • Peak Signal-to-noise Ratio
    • PSNR
    • Individual Structures
    • Image Preprocessing
    • Crossover Probability
    • Denoising Methods
    • Tournament Selection
    • Pixel Measurements
• isbn_formats:
  • format: CD, value: 978-1-4244-6587-3, isbnType: New-2005
  • format: Print ISBN, value: 978-1-4244-6586-6, isbnType: New-2005
  • format: Electronic ISBN, value: 978-1-4244-6588-0, isbnType: New-2005
• authors:
  • Author Name: Deng Zhang
    Affiliation: Graduate School of Information, Production and Systems, Waseda University, Fukuoka, Japan
    Author URL: https://ieeexplore.ieee.org/author/37087516527
    ID: 37087516527
    Order: 1
    Author Affiliations:
    • Graduate School of Information, Production and Systems, Waseda University, Fukuoka, Japan
  • Author Name: Shingo Mabu
    Affiliation: Graduate School of Information, Production and Systems, Waseda University, Fukuoka, Japan
    Author URL: https://ieeexplore.ieee.org/author/37272290100
    ID: 37272290100
    Order: 2
    Author Affiliations:
    • Graduate School of Information, Production and Systems, Waseda University, Fukuoka, Japan
  • Author Name: Kotaro Hirasawa
    Affiliation: Graduate School of Information, Production and Systems, Waseda University, Fukuoka, Japan
    Author URL: https://ieeexplore.ieee.org/author/37280651500
    ID: 37280651500
    Order: 3
    Author Affiliations:
    • Graduate School of Information, Production and Systems, Waseda University, Fukuoka, Japan

Image Sensor

• sensor_type: CMOS
• resolution: 256x256
• 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: The proposed method will contribute to the denoising in CMOS image sensors.
• benefits: The method is effective in AWGN reduction and edge preservation, enhancing image quality.

Supporting Organizations

• supported_by: Waseda University

Manuscript Details

• publication_date: 10-13 Oct. 2010

Relevancy Score

• score: 9
• missing_fields:
  • dynamic_range
  • power_consumption
  • dark_current
  • focal_length
  • aperture
  • field_of_view
  • distortion
  • frame_rate
  • sensitivity
  • shutter_speed
  • noise

Processed JSON Filename

request_6cf90470-25f4-4d0f-81d6-5f7df899ed86-noise_reduction_using_genetic_algorithm_based_pcnn_method.json