Photocurrent Estimation from Multiple Non-destructive Samples in a CMOS Image Sensor

CMOS image sensors generally su er from lower dynamic range than CCDs due to their higher readout noise. Their high speed readout capability and the potential of integrating memory and signal processing with the sensor on the same chip, open up many possibilities for enhancing their dynamic range. Earlier work have demonstrated the use of multiple non-destructive samples to enhance dynamic range, while achieving higher SNR than using other dynamic range enhancement schemes. The high dynamic range image is constructed by appropriately scaling each pixel's last sample before saturation. Conventional CDS is used to reduce o set FPN and reset noise. This simple high dynamic range image construction scheme, however, does not take full advantage of the multiple samples. Readout noise power, which doubles as a result of performing CDS, remains as high as in conventional sensor operation. As a result dynamic range is only extended at the high illumination end. The paper explores the use of linear mean-square-error estimation to more fully exploit the multiple pixel samples to reduce readout noise and thus extend dynamic range at the low illumination end. We present three estimation algorithms: (1) a recursive estimator when reset noise and o set FPN are ignored, (2) a non-recursive algorithm when reset noise and FPN are considered, and (3) a recursive estimation algorithm for case (2), which achieves mean square error close to the non-recursive algorithm without the need to store all the samples. The later recursive algorithm is attractive since it requires the storage of only a few pixel values per pixel, which makes its implementation in a single chip digital imaging system feasible.