US5844627A
Expired / Lapsed
US
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Structure and method for reducing spatial noise
Assignee: Minerva Systems Inc
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Filing: 1995-09-11
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Granted: 1998-12-01
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Expires: 2015-09-11
Abstract
Abstract A digital filter for noise reduction selects between local variances obtained from adjacent pixels in the same frame and adjacent pixels in the same field. In one embodiment, the digital filter includes a filter modified from an adaptive Wiener filter which preserves edges and smoothes smooth areas of the image. A high compression ratio can be achieved in very smooth regions of the image without introducing artifacts.
Inventors
- 👤 William May
- 👤 Jean-Georges Fritsch
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-09 14:16
CPC Classifications
H; H04; H04N; H04N5/00; H04N5/14; H04N5/21
Claims Analysis
Total Claims: 12
Claim 1
Independent Claim
141 words
A digital filter for removing noise in a video image, said video image formed by successive frames, each frame including first and second fields, each field including multiple pixels, said digital filter comprising: means for computing, for each pixel of a frame of said video image, (i) a first variance based on a first group of pixels, selected from said frame, adjacent said pixel; and (ii) a second variance based on a second group of pixels, adjacent said pixel and equal in number as said first group of pixels, selected from the field which includes said pixel; means for selecting, as a neighborhood variance, the lesser of said first variance and said second variance; and an adaptive filter, receiving said video image and said neighborhood variance, said adaptive filter having a filter coefficient which is a function of said neighborhood variance.
Claim 2
Dependent Claim
141 words
A digital filter for removing noise in a video image, said video image formed by successive frames, each frame including first and second fields, each field including multiple pixels, said digital filter comprising: means for computing, for each pixel of a frame of said video image, (i) a first variance based on a first group of pixels, selected from said frame, adjacent said pixel; and (ii) a second variance based on a second group of pixels, adjacent said pixel and equal in number as said first group of pixels, selected from the field which includes said pixel; means for selecting, as a neighborhood variance, the lesser of said first variance and said second variance; and an adaptive filter, receiving said video image and said neighborhood variance, said adaptive filter having a filter coefficient which is a function of said neighborhood variance.
Claim 3
Dependent Claim
16 words
A digital filter as in claim 1, wherein said adaptive filter being an adaptive Wiener filter.
Claim 4
Dependent Claim
30 words
A digital filter as in claim 1, wherein said first and second groups of pixels each form a 3×3 configuration centered at said pixel in a respective frame or field.
Claim 5
Dependent Claim
31 words
A digital filter as in claim 1, wherein said adaptive filter has filter characteristics that preserve sharpness of edges and smooth regions of said video image having a relatively low variance.
Claim 6
Dependent Claim
69 words
A digital filter as in claim 1, further comprising: means for computing a first mean based on said first group of pixels; and means for computing a second mean based on said second group of pixels; wherein said means for selecting selects, as a neighborhood mean, from said first mean and said second mean, the mean computed from the same group of pixels used to compute said neighborhood variance.
Claim 7
Independent Claim
135 words
A method for removing noise in a video image, said video image formed by successive frames, each frame including first and second fields, each field including multiple pixels, said method comprising: computing, for each pixel of a frame of said video image, a first variance based on a first group of pixels, in said frame, adjacent said pixel; computing, for each pixel of said frame of said video image, a second variance based on a second group of pixels, selected from the field which includes said pixel and equal in number as said first group of pixels, adjacent said pixel; selecting the lesser of said first variance and said second variance as a neighborhood variance; and filtering said pixel using an adaptive filter having a filter coefficient which is a function of said neighborhood variance.
Claim 8
Independent Claim
135 words
A method for removing noise in a video image, said video image formed by successive frames, each frame including first and second fields, each field including multiple pixels, said method comprising: computing, for each pixel of a frame of said video image, a first variance based on a first group of pixels, in said frame, adjacent said pixel; computing, for each pixel of said frame of said video image, a second variance based on a second group of pixels, selected from the field which includes said pixel and equal in number as said first group of pixels, adjacent said pixel; selecting the lesser of said first variance and said second variance as a neighborhood variance; and filtering said pixel using an adaptive filter having a filter coefficient which is a function of said neighborhood variance.
Claim 9
Dependent Claim
15 words
A method as in claim 6, wherein said adaptive filter being an adaptive Wiener filter.
Claim 10
Dependent Claim
33 words
A method as in claim 6, wherein said first group of pixels and said second group of pixels each form a 3×3 configuration centered at said pixel in a respective frame or field.
Claim 11
Dependent Claim
30 words
A method as in claim 1, wherein said adaptive filter has filter characteristics that preserve sharpness of edges and smooth regions of said video image having a relatively low variance.
Claim 12
Dependent Claim
62 words
A method as in claim 1, further comprising the steps of: computing a first mean based on said first group of pixels; computing a second mean based on said second group of pixels; and selecting, as a neighborhood mean, from said first mean and said second mean, the mean computed from the same group of pixels used to compute said neighborhood variance.
References Cited (Backward Citations)
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Cited
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Cited
USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 2001-11-30 | 2002-06-01 | 2002-12-01 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2005-11-30 | 2006-06-01 | 2006-12-01 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2009-11-30 | 2010-06-01 | 2010-12-01 | $7700.00 | Window Closed / Lapsed |