A Nonlocal Maximumlikelihood Denoising Algorithm

• doi: 10.1109/AERO.2007.353029
• title: A Non-local Maximum-Likelihood Denoising Algorithm
• publisher: IEEE
• isbn: 1-4244-0525-4
• issn: 1095-323X
• partnum: 07TH8903
• rank: 853
• access_type: LOCKED
• content_type: Conferences
• abstract: Digital images obtained using CCD or CMOS sensors are subject to corruption by AWGN noise during sensor readout. Most of the techniques used for noise reduction in images have relied on methods that operate on global image attributes and noise assumptions. Recently, several approaches have been proposed that attempt to recover an un-corrupted image by examining attributes within a statistical neighborhood of an image. This paper will extend this body of work by describing a novel statistical neighborhood algorithm for denoising images. This algorithm exploits the naturally occurring redundancy in an image and employs an algorithm to selectively normalize and average redundant information in the corrupted image.
• article_number: 4161439
• pdf_url: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4161439
• html_url: https://ieeexplore.ieee.org/document/4161439/
• abstract_url: https://ieeexplore.ieee.org/document/4161439/
• publication_title: 2007 IEEE Aerospace Conference
• conference_location: Big Sky, MT
• conference_dates: 3-10 March 2007
• publication_number: 4161231
• is_number: 4144550
• publication_year: 2007
• publication_date: 3-10 March 2007
• start_page: 1
• end_page: 7
• citing_paper_count: 2
• citing_patent_count: 0
• download_count: 115
• insert_date: 20070618
• index_terms:
  • ieee_terms:
    • Noise reduction
    • Additive white noise
    • Gaussian noise
    • Frequency
    • Background noise
    • Thermal sensors
    • CMOS image sensors
    • Sensor phenomena and characterization
    • AWGN
    • Additive noise
  • dynamic_index_terms:
    • Denoising Algorithm
    • Additive Noise
    • Additive White Gaussian Noise
    • Normal Distribution
    • Gaussian Distribution
    • Degrees Of Freedom
    • Mean Square Error
    • Mean Square Deviation
    • Gaussian Noise
    • Image Pixels
    • Image Size
    • Mean Of Distribution
    • Noise Variance
    • Absence Of Knowledge
    • Real Noise
    • Image Block
    • Non-centrality Parameter
• isbn_formats:
  • format: CD, value: 1-4244-0525-4, isbnType: Historical
  • format: Print ISBN, value: 1-4244-0524-6, isbnType: Historical
• authors:
  • Author Name: Matthew D. Sambora
    Affiliation: Air Force Institute of Technology, OH, USA
    Author URL: https://ieeexplore.ieee.org/author/37085466865
    ID: 37085466865
    Order: 1
    Author Affiliations:
    • Air Force Institute of Technology, OH, USA

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: 3-10 March 2007

Relevancy Score

• score: 8
• 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
  • publication_date

Processed JSON Filename

request_394f6195-21c5-4102-8d49-d71c298afe0b-a_nonlocal_maximumlikelihood_denoising_algorithm.json