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Dileepan Joseph and Steve Collins Department of Engineering Science University of Oxford, England

Modelling, calibration and correction of nonlinear illumination-dependent fixed pattern noise in logarithmic CMOS image sensors. Dileepan Joseph and Steve Collins Department of Engineering Science University of Oxford, England. Outline. Logarithmic image sensors Pixel modelling

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Dileepan Joseph and Steve Collins Department of Engineering Science University of Oxford, England

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  1. Modelling, calibration and correction of nonlinear illumination-dependent fixed pattern noise in logarithmic CMOS image sensors Dileepan Joseph and Steve Collins Department of Engineering Science University of Oxford, England

  2. Outline • Logarithmic image sensors • Pixel modelling • Fixed pattern noise (FPN) • Sensor calibration • Image correction • Summary and conclusions IMTC, Budapest (IEEE)

  3. Logarithmic image sensors • CMOS versus CCD image sensors • Electronics placed on same die as pixels • Cost, power consumption, size, weight • Quantum efficiency, yield, price pressure • Logarithmic versus linear pixels • Continuous sensing, random access • High dynamic range, low bit rate • Resolution, sensitivity, frame rate IMTC, Budapest (IEEE)

  4. Physical model Abstract model Pixel modelling IMTC, Budapest (IEEE)

  5. Fixed pattern noise (FPN) • Offset (aj) variation (1  j  N pixels) • Offset and gain (bj) variation • Offset, gain and bias (cj) variation IMTC, Budapest (IEEE)

  6. Sensor calibration • Calibrate the sensor with images yij of M uniform illuminances xi (e.g. white paper) • Extract parameters by minimising the mean square error IMTC, Budapest (IEEE)

  7. Sensor calibration cont’d • Calibration error is 3.9, 1.9 and 0.9 for one, two and three parameter models • Calibration error versus illuminance differs markedly IMTC, Budapest (IEEE)

  8. Image correction • Left to right: original plus one, two and three parameter FPN corrected images • Top to bottom: apertures of 1.8, 4, 8 and 16 f-stops • Inter-scene plus intra-scene dynamic range equals 67 dB IMTC, Budapest (IEEE)

  9. Image correction cont’d IMTC, Budapest (IEEE)

  10. Summary and conclusions • Physical and abstract pixel model • Offset, gain, bias and error • Parameter variation causes FPN • Calibration with uniform illuminance • Results indicate FPN is nonlinear • FPN correction is necessary • Digital correction of images • More robust analogue circuits IMTC, Budapest (IEEE)

  11. Acknowledgements • Many thanks to the Natural Sciences and Engineering Research Council of Canada and the Engineering and Physical Sciences Research Council of Britain for their generous support. IMTC, Budapest (IEEE)

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