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. n V Decoder TG (Condenser, CDS, Decoder) Programmable Gain Amp Dark noise （ C ） Dark shot noise （ W ） Photon shot noise （ W ） Condensing Gm （ C ） Amp noise （ W ） Offset noise （ C ） Reset noise （ W ） 1/f noise(W ） Image pixel Amp noise （ W ） Offset noise （ C ） Condensing Gm （ C ） 1/f noise （ W ） Analog circuit Noise in image sensor RST SEL Tx Col. Bus N P N+ CMOS image sensor W white noise, C colored noise.

3. Principle of an ACF The data is collected at the same interval time. The data is collected at the same interval time. Autocorrelation value is calculated according to the following equation. Autocorrelation value is calculated according to the following equation. 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 (B) white noise wave Original wave ACF value Make base wave Make base wave Make noise wave Make noise wave Sampling In same interval time Sampling In same interval time ＋ ＋ (A) cosine wave (B) white noise wave white noise wave Calculation ACF value Calculation ACF value Block diagram of 1-D ACF method

5. Expansion ACF method to 2-D model Time (a.u) H direction Ｉｍａｇｅ Time H V 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 Flame Number Pixel- Ａ Pixel-B Bright pixel A (180 in 256 scale) and dark pixel B (8 in 256 scale) Auto Correlation Value (A) (B)

7. The algorithm of noise judging and filtering process by a time domain ACF method Image data (BMP,RAW) Pixel value extraction Calculation of ACF Leveling filter processing END Pixel value decision Pixel value i<10 ACF value r<0.8 I< Total pixel number No

8. Result of image processing Original image Processing image ・ Reduction of random noise is possible per pixel. ・ Since filter processing is not performed in a bright pixel, resolution does not deteriorate.

9. Image data (BMP,RAW) Pixel value extraction Calculation of ACF Leveling filter processing END Pixel value decision Pixel value i<10 ACF value r<0.8 I< Total pixel number No The algorithm and the example of processing of a time domain ACF method

10. Original Ith= 100 Rth=0.985 Ith= 100 Rth=0.985 Ith= 100 Rth=0.995 Ith= 100 Rth=1.000 Image processing result as a function of threshold value both pixel value and ACF value