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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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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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