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(2) Sony Computer Science Laboratories, Inc. 3-14-13 Higashi Gotanda, Shinagawa-ku, Tokyo, Japan
(3) École Polytechnique, LIX, F-91128 Palaiseau Cedex, France
(4) Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu, Kitakyushu, Fukuoka, Japan
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1. Noise reduction in CMOS image sensors
for high quality imaging: The
autocorrelation function filter on burst
image sequences
Kazuhiro Hoshino1, Frank Nielsen2,3, Toshihiro Nishimura4
1 Image Sensor Business Group, Sony Corporation,
4-14-1 Asahi-chou, Atsugi-shi, Kanagawa, Japan
Kazuhiro.Hoshino@jp.sony.com,
2 Sony Computer Science Laboratories, Inc.
3-14-13 Higashi Gotanda, Shinagawa-ku, Tokyo, Japan
Frank.Nielsen@acm.org
3 Ecole Polytechnique, LIX F-91128 Palaiseau Cedex, France
4 Graduate School of Information, Production and Systems, Waseda University
2-7 Hibikino, Wakamatsu, Kitakyushu, Fukuoka, Japan
toshi-hiro@waseda.jp
2. Noise in image sensor
RST
Tx
Image pixel
P Offset noise(C)
N+
Reset noise(W)
N
SEL
Col. Bus 1/f noise(W )
Dark noise(C)
Dark shot noise( W )
n Photon shot noise( W )
Condensing Gm(C)
V Decoder
Amp noise( W)
Analog circuit
(Condenser, CDS, Decoder) Amp noise( W )
Offset noise(C)
Condensing Gm(C)
1/f noise( W)
TG Programmable
Gain Amp
CMOS image sensor W white noise, C colored noise.
3. Principle of an ACF
The data is collected at the same interval time.
Autocorrelation value is calculated according to the following equation.
N 1
1
R( ) x(t ) x(t )
N t 0
R is ACF value.
N is the number of data,
t is time.
x is pixel value,
and τ is shifted time.
4. 1D simulation of ACF
(A) cosine wave
Block diagram of 1-D ACF method
(B) white noise wave
Sampling
Make Calculation
+ In same
base wave ACF value
interval time
Make
noise wave
white noise wave
Original
wave
ACF
value
(A) cosine wave (B) white noise wave
5. Expansion ACF method to 2-D model
Time
V
Image
H H direction
N 1
1
R( ) x(t ) x(t )
N t 0
R is ACF value.
N is the number of data which were sampled in time axis,
t is time.
x is pixel value,
and τ is shifted time.
6. ACF value as a function of pixel intensity
(A)
Auto Correlation Value Pixel-A
(B)
Pixel-B
Flame Number
Bright pixel A (180 in 256 scale) and dark pixel B (8 in 256 scale)
7. The algorithm of noise judging and filtering process
by a time domain ACF method
Image data (BMP,RAW)
Pixel value extraction
Pixel value i<10 No
Calculation of ACF
ACF value r<0.8 No
Leveling filter processing
Pixel value decision
I< Total pixel number
No
END
8. Result of image processing
・ Reduction of random noise is possible per pixel.
・ Since filter processing is not performed in a bright pixel, resolution does not
deteriorate.
Original image Processing image
9. The algorithm and the example of processing of a
time domain ACF method
Image data (BMP,RAW)
Pixel value extraction
Pixel value i<10 No
Calculation of ACF
ACF value r<0.8 No
Leveling filter processing
Pixel value decision
I< Total pixel number
No
END
10. Image processing result as a function of threshold
value both pixel value and ACF value
Original Ith= 100
Rth=0.985
Ith= 100 Ith= 100
Rth=0.995 Rth=1.000