Joint Demosaicing And Denoising

• doi: 10.1109/ICIP.2005.1530390
• title: Joint demosaicing and denoising
• publisher: IEEE
• isbn: 0-7803-9134-9
• issn: 2381-8549
• rank: 2844
• access_type: LOCKED
• content_type: Conferences
• abstract: The output image of a digital camera is subject to a severe degradation due to noise in the image sensor. This paper proposes a novel technique to combine demosaicing and denoising procedures systematically into a single operation by exploiting their obvious similarities. We first design a filter as if we are optimally estimating a pixel value from a noisy single-color image. With additional constraints, we show that the same filter coefficients are appropriate for CFA interpolation (demosaicing) given noisy sensor data. The proposed technique can combine many existing denoising algorithms with the demosaicing operation. In this paper, a total least squares denoising method is used to demonstrate the concept. The algorithm is tested on color images with pseudo-random noise and on raw sensor data from a real CMOS digital camera that we calibrated. The experimental results confirm that the proposed method suppresses noise (CMOS image sensor noise model) while effectively interpolating the missing pixel components, demonstrating a significant improvement in image quality when compared to treating demosaicing and denoising problems independently.
• article_number: 1530390
• pdf_url: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1530390
• html_url: https://ieeexplore.ieee.org/document/1530390/
• abstract_url: https://ieeexplore.ieee.org/document/1530390/
• publication_title: IEEE International Conference on Image Processing 2005
• conference_location: Genova, Italy
• conference_dates: 14-14 Sept. 2005
• publication_number: 10242
• is_number: 32662
• publication_year: 2005
• publication_date: 14-14 Sept. 2005
• start_page: III
• end_page: 309
• citing_paper_count: 7
• citing_patent_count: 0
• download_count: 367
• insert_date: 20060327
• index_terms:
  • ieee_terms:
    • Noise reduction
    • CMOS image sensors
    • Digital cameras
    • Filters
    • Pixel
    • Degradation
    • Image sensors
    • Interpolation
    • Least squares methods
    • Testing
  • dynamic_index_terms:
    • Joint Demosaicing
    • Digital Camera
    • Image Quality
    • Sensor Data
    • Color Images
    • Pseudo-random
    • Noisy Data
    • Image Sensor
    • Camera Sensor
    • Single Operation
    • Optimal Estimation
    • Optimal Approximation
    • Improve Image Quality
    • Sensor Noise
    • Noisy Images
    • Raw Sensor Data
    • Denoising Methods
    • Raw Files
    • Linear Approximation
    • Best Linear Unbiased Estimates
    • Linear Estimation
    • High-pass Filter
    • Singular Value Decomposition
    • Image Patches
    • Green Image
    • Blue Values
    • Pixel Of Interest
    • Sensor Pixel
    • Blue Pixels
    • Sensor Values
    • Eigendecomposition
    • Pixel Location
    • Red Values
    • Ideal Sensor
• isbn_formats:
  • format: Print ISBN, value: 0-7803-9134-9, isbnType: Historical
• authors:
  • Author Name: K. Hirakawa
    Affiliation: New England Conservatory of Music, Boston, MA, USA
    Author URL: https://ieeexplore.ieee.org/author/37295032600
    ID: 37295032600
    Order: 1
    Author Affiliations:
    • New England Conservatory of Music, Boston, MA, USA
  • Author Name: T.W. Parks
    Affiliation: Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA
    Author URL: https://ieeexplore.ieee.org/author/37283502800
    ID: 37283502800
    Order: 2
    Author Affiliations:
    • Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA

Image Sensor

• sensor_type: CMOS
• resolution: 300K pixels
• 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

• noise: CMOS image sensor noise model

Applications & Benefits

• cell_imaging: used in medical devices
• benefits: enables digital imaging in smartphones, medical devices, and automotive systems.

Supporting Organizations

• supported_by: Texas Instruments, Agilent Technologies, Dr. B. Guntur

Manuscript Details

• publication_date: 14-14 Sept. 2005

Relevancy Score

• score: 9
• missing_fields:
  • fill factor
  • quantum efficiency
  • readout speed
  • noise sources
  • analog-to-digital conversion techniques
  • microlenses
  • on-chip colour filters
  • global or rolling shutters
  • backside illumination

Processed JSON Filename

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