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Noise Reduction in CMOS Image Sensors: Autocorrelation Function Filter

Implementing the autocorrelation function filter on burst image sequences can enhance the quality of imaging in CMOS image sensors. This method involves collecting data at fixed intervals, calculating autocorrelation values, and applying noise reduction techniques to improve image quality without compromising resolution.

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Noise Reduction in CMOS Image Sensors: Autocorrelation Function Filter

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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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