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Kazuhiro Hoshino1, Frank Nielsen2 , 3, Toshihiro Nishimura4

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

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Kazuhiro Hoshino1, Frank Nielsen2 , 3, Toshihiro Nishimura4

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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. RST Tx P Image pixel N+ N SEL Offset noise（C） Reset noise（W） 1/f noise(W） Col. Bus Dark noise（C） Dark shot noise（ W） Photon shot noise（ W） Condensing Gm（C） Amp noise（ W） n V Decoder Analog circuit (Condenser, CDS, Decoder) Amp noise（ W） Offset noise（C） Condensing Gm（C） 1/f noise（ W） TG Programmable Gain Amp Noise in image sensor 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. 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 Block diagram of 1-D ACF method (A) cosine wave (B) white noise wave Sampling In same interval time Calculation ACF value Make base wave ＋ Make noise wave white noise wave Original wave ACF value (B) white noise wave (A) cosine wave

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

  7. 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 The algorithm of noise judging and filtering process by a time domain ACF method

  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. Processing image Original image

  9. 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 The algorithm and the example of processing of a time domain ACF method

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

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