Fast Method for Noise Level Estimation and Denoising
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Transcript of Fast Method for Noise Level Estimation and Denoising
8/7/2019 Fast Method for Noise Level Estimation and Denoising
http://slidepdf.com/reader/full/fast-method-for-noise-level-estimation-and-denoising 1/2
5.3-1
Fast Method forNoise Level Estimation and Denoising
Angelo Bosco, Arcangelo Bruna, Ciuseppe Messina, Giuseppe SpampinatoSTMicroelectronics, Advanced System Technology, ImagingGroup, Catania Lab, Italy
Abstract-- This paper describes a novel technique for fast noiselevel estimation that can be used both in spatial and temporalfilters to tune the filtering strength. The method has been
validated by integrating it in a denoising algorithm for jointGaussian noise reduction and defect correction in raw digitalimages.
I. INTRODUCTION
Images acquired using low cost imagers are particularly
prone to noise problems, especially un der low light conditions.
Th e unwanted signal can be a mixture of Gaussian and
impulsive noise along with generally defective stuck pixels.
Noise reduction filters usually rely on the detected noise
standard deviation o [ 3 which helps in the removal of outliers
and in the reduction o f Gaussian noise.
A method for fast noise levet estimation is presented, which,
coupled with an algorithm for joint Gaussian noise reduction
and defects correction, allows the complete filtering of an
image using low computational resources. The image is
supposed to be corrupted by Additive White Gaussian Noise
( A W G V .
Most of the samples of a Gaussian distribution (68%) fall in
the interval Cp-0, pw ] and approximately 99% are contained
within three standard deviations from the mean 1.1. Th e
presented method is based on the aforementioned properties
and requires only a few comparisons and sums; hence it is
suitable form real-time applications and far devices having low
power constraints.
11. PROPOSED SOLUTION
Without loosing generality and within a certain degree of
confidence, it is possible to assume that an image cannot be
affected by an arbitrary high noise level 6.
Hence, a omWalue can be set; i.e. we start from the
hypothesis that non-outlier pixels will not deviate more than
omaxrom their correct value.
Processing the raw Bayer 121 image on a pixel basis, the
absolute differences de d,, ..., d, between the central pixel and
its neighborhood are computed (Fig. I) .
If the above 8 differences are below the noise threshold, we
can reasonably assume that a flat area has been detected; in
this case, each absolute difference is plotted in a noise
histogram that is used for the estimation of 6.
The noise samples d, are counted and accumulated in the
corresponding histogram bin.
0-7803-8838-0/05/%20,00 2005 IEEE.
Fig. 1. Filter mask in the Ba ya data (greencase).
After processing the whole image, the noise h i s t o g ” will be
hal f Gaussian-like distributed.
Fig. 2 shows the noise histogram obtained by analyzing a
raw B ayer image processing e ach color channel separately.
f
t
I
Fig. 2 Normalized histogram of a Buyer image
Note that we compensated the fact that the green elements
are twice the number of red and blue samples by normalizing
the histogram. Fig. 2 also shows that the blue channel contains
higher noise if compared to the green and red channels. This is
consistent with the fact that usually, the blue channel is the
worst.
Once the population of the histogram is completed, it is
possible to estimate the noise level CJ by counting the
histogram elements until the 68% of the total samples has been
reached.
The index on the x-axis of the histogram allowing reaching
th e 68% of the samples represents the estimated noise level(Fig. 3) . Image structure interference is mostly avoided
because the histogram is updated using only areas in which the
absolute differences are all less than the noise maximum
threshold.
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8/7/2019 Fast Method for Noise Level Estimation and Denoising
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IFig. 3 Esttmation of 0 .
The algorithm has been validated in a system for joint
Gaussian noise reduction and outliers detection shown in Fig.
4. The noise level estimator is used in the preprocessing block
which analyzes the sensor raw CFA data.
Fig. 4 Architectural scheme.
Specifically, by considering the d biased versions o f the
central pixel it is possibleto determine if the central element is
an outlier or not. As illustrated in F i g s , if the biased
version of P is brighter than the brightest pixel in the
neighborhood, then P recognized as a spike. A similar
reasoning holds in the case of the +(T biased version for the
detection of dead e lements in the sensor array data.
P-a P Pw
Fig. 5 CentralPixel Outlier Detection.
Afier removing the outher in the filter mask, the ra w data is
further processed to reduce Gaussian noise for example using
the algorithm described in [3][4]; the noise estimation
computed with the proposed method is used to regulate the
filter strength. Results of filtering are shown in Fig. 6; the
original noisy image (Fig.6a) contains spikes, dea d pixels and
Gaussian noise. The processed image (FigQb) contains no
defective elements: furthermore pixel fluctuations due to
Gaussian noise ar e also reduced.
(a) miFigure 6 (a) Bayer data with detective elements.(b) Corrected Bayer image
plus Gaussian Filtering.
111. EXPERIMENTAL RESULTS
Simulations results (Fig.7) demonstrated that after tuning
the algorithm for a particular sensor, the estimation becomes
more and more reliable. Fig. 7 shows noise level estimation
results (NLEI, NLE2, NLE3) that depend on different
maximum noise level thresholds used in the tuning phase; NLrepresents the true noise level.
Fig. 7 Simulation results.
The reliability of the method has been validated by
successhlly incorporating the estimation routine into an
algorithm for Gaussian noise reduction and outliers removal.
REFERENCES
A. Amer, E. Dubois, A. Mitiche, “Relmbleand Fast SfrucrurednentedVideo
Noise Estimation”, E E E ICIP 2002.B.E. Bayer, “Color lmaging Anq” , U.S. Patent 3971,0651976.
A.Bosco, K.Findlater. S.Battiato, A.Castorina, “A Temporal Noise
Reduction Filter Based on Image Sensor Full-Frame Dard’,
Proceedings f ICCE2003,A.Bosco, K.Findlater, S.Battiato, A.Castorina “A Noise Red!lCtZOh
Filter fo r Full-Frame Dofa Imagtng Devices”. lEEE Transactions onConsumer Electronics. Vol. 49, No.3, August 2003.
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