WO2002001855A3 - Apparatus and method for adaptively reducing noise in a noisy input image signal - Google Patents

Apparatus and method for adaptively reducing noise in a noisy input image signal Download PDF

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Publication number
WO2002001855A3
WO2002001855A3 PCT/CA2001/000934 CA0100934W WO0201855A3 WO 2002001855 A3 WO2002001855 A3 WO 2002001855A3 CA 0100934 W CA0100934 W CA 0100934W WO 0201855 A3 WO0201855 A3 WO 0201855A3
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WO
WIPO (PCT)
Prior art keywords
noise
configuration
snr
local
adaptive
Prior art date
Application number
PCT/CA2001/000934
Other languages
French (fr)
Other versions
WO2002001855A2 (en
Inventor
Dinh Chon Tam Le
Cong Toai Kieu
Ha Do Viet
Original Assignee
Miranda Technologies Inc
Dinh Chon Tam Le
Cong Toai Kieu
Ha Do Viet
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Miranda Technologies Inc, Dinh Chon Tam Le, Cong Toai Kieu, Ha Do Viet filed Critical Miranda Technologies Inc
Priority to AU2001270392A priority Critical patent/AU2001270392A1/en
Publication of WO2002001855A2 publication Critical patent/WO2002001855A2/en
Publication of WO2002001855A3 publication Critical patent/WO2002001855A3/en

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N5/00—Details of television systems
    • H04N5/14—Picture signal circuitry for video frequency region
    • H04N5/21—Circuitry for suppressing or minimising disturbance, e.g. moiré or halo
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/20—Image enhancement or restoration by the use of local operators
    • G06T5/70—
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/30—Noise filtering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20004—Adaptive image processing
    • G06T2207/20012—Locally adaptive
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20021—Dividing image into blocks, subimages or windows
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20076—Probabilistic image processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20172—Image enhancement details
    • G06T2207/20192—Edge enhancement; Edge preservation

Abstract

The basic configuration of Single local Adaptive Window Spatial Noise Reducer (SAW-SNR 318) is based on a preliminary de-noising low-pass filter followed by homogenous region segmentation to the considered pixel in a given local window. The configuration is composed also of an adaptive local mean estimator (307), an adaptive local statistic estimator (312) which is preferably an economic standard deviation (SD) estimator and finally, a minimum-mean-square-error (MMSE) based de-noising technique. The proposed segmentation configuration outperforms existing spatial noise reducers in term of subjective and objective performances, in term of edge preservation, noise reduction in both homogenous regions or picture edges and Peak Signal to noise Ratio (PSNR). A second configuration in the form of a Parallel Multiple local Adaptive Window Spatial Noise Reducing apparatus (Parallel M-AW-SNR), is a combination of several basic reducers (312) which implements different segmented windows. The M-AW-SNR, which is the less complex configuration for multiple spatial noise reducers, reduces further residual noise as compared to the basic configuration of SAW-SNR (312). A third configuration combines the basic configuration of SAW-SNR (312) with a controllable noise variance estimator. This generic configuration allows an adaptive local control of noise reduction level, which can be useful for some correlated noise such as ringing noise in DCT-based decompressed images or cross-luminance noise in composite decoded images.
PCT/CA2001/000934 2000-06-26 2001-06-22 Apparatus and method for adaptively reducing noise in a noisy input image signal WO2002001855A2 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
AU2001270392A AU2001270392A1 (en) 2000-06-26 2001-06-22 Apparatus and method for adaptively reducing noise in a noisy input image signal

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US09/603,364 US6633683B1 (en) 2000-06-26 2000-06-26 Apparatus and method for adaptively reducing noise in a noisy input image signal
US09/603,364 2000-06-26

Publications (2)

Publication Number Publication Date
WO2002001855A2 WO2002001855A2 (en) 2002-01-03
WO2002001855A3 true WO2002001855A3 (en) 2002-08-08

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Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CA2001/000934 WO2002001855A2 (en) 2000-06-26 2001-06-22 Apparatus and method for adaptively reducing noise in a noisy input image signal

Country Status (3)

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US (1) US6633683B1 (en)
AU (1) AU2001270392A1 (en)
WO (1) WO2002001855A2 (en)

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AU2001270392A1 (en) 2002-01-08
WO2002001855A2 (en) 2002-01-03
US6633683B1 (en) 2003-10-14

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