U.S. patents available from 1976 to present.
U.S. patent applications available from 2005 to present.

Digital image noise reduction of luminance and chrominance based on overlapping planar approximation

Patent 5563963 Issued on October 8, 1996. Estimated Expiration Date: Icon_subject October 8, 2013. Estimated Expiration Date is calculated based on simple USPTO term provisions. It does not account for terminal disclaimers, term adjustments, failure to pay maintenance fees, or other factors which might affect the term of a patent.

Patent References

Noise cleaner for binary images
Patent #: 4510618
Issued on: 04/09/1985
Inventor: Ataman ,   et al.

Character reading device
Patent #: 4520505
Issued on: 05/28/1985
Inventor: Yamamoto ,   et al.

Digital image correction
Patent #: 4731865
Issued on: 03/15/1988
Inventor: Sievenpiper

Image data processing method and device therefor
Patent #: 4734770
Issued on: 03/29/1988
Inventor: Matsuba

Unsharp masking for image enhancement
Patent #: 4794531
Issued on: 12/27/1988
Inventor: Morishita ,   et al.

Image signal filtering
Patent #: 4817180
Issued on: 03/28/1989
Inventor: Cho ,   et al.

Method of smoothing image signals
Patent #: 4827533
Issued on: 05/02/1989
Inventor: Tanaka

Binarizing apparatus
Patent #: 4903316
Issued on: 02/20/1990
Inventor: Hongo, et al.

Method and apparatus for recognizing pattern of gray level image
Patent #: 4941192
Issued on: 07/10/1990
Inventor: Mishima, et al.

System and method for reducing digital image noise Patent #: 5091972
Issued on: 02/25/1992
Inventor: Kwon, et al.

Inventors

Application

No. 781467 filed on 10/23/1991

US Classes:

382/266, Edge or contour enhancement358/463, Noise elimination382/275Artifact removal or suppression (e.g., distortion correction)

Examiners

Primary: Boudreau, Leo H.
Assistant: Johns, Andrew W.

Attorney, Agent or Firm

International Class

G06K 009/40

Abstract

The present invention reduces noise in digital photographic images based on the assumption that images may be decomposed into two types of regions, smooth regions and edge regions. Smooth regions are areas of the image lacking any sharp detail, such as blue sky. Edge regions are regions containing sharp detail, such as edges and textured regions (such as grass). The present method reduces noise in the smooth regions by a mathematical blurring technique based on least squares regression. The blurring does not degrade the sharpness of the image, because there are no sharp details in the smooth regions. Edge regions are left undisturbed to maintain sharpness, but the noise is less noticeable in those regions than in the smooth regions. The method operates upon the luminance and chrominance component of a digital image.

Other References

  • Panda et al., "Recursive Least Squares Smoothing of Noise in Images," IEEE Transactions on Acoustics, Speech, and Signal Proc., vol. ASSP-25, No. 6, Dec. 1977, pp. 520-52
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