Using polynomials to simplify fixed pattern noise and photometric correction of logarithmic CMOS image sensors.
An important class of complementary metal-oxide-semiconductor (CMOS) image sensors are those where pixel responses are monotonic nonlinear functions of light stimuli. This class includes various logarithmic architectures, which are easily capable of wide dynamic range imaging, at video rates, but which are vulnerable to image quality issues. To minimize fixed pattern noise (FPN) and maximize photometric accuracy, pixel responses must be calibrated and corrected due to mismatch and process variation during fabrication. Unlike literature approaches, which employ circuit-based models of varying complexity, this paper introduces a novel approach based on low-degree polynomials. Although each pixel may have a highly nonlinear response, an approximately-linear FPN calibration is possible by exploiting the monotonic nature of imaging. Moreover, FPN correction requires only arithmetic, and an optimal fixed-point implementation is readily derived, subject to a user-specified number of bits per pixel. Using a monotonic spline, involving cubic polynomials, photometric calibration is also possible without a circuit-based model, and fixed-point photometric correction requires only a look-up table. The approach is experimentally validated with a logarithmic CMOS image sensor and is compared to a leading approach from the literature. The novel approach proves effective and efficient.
Innovation Centre for Engineering, University of Alberta, 9211 116 Street NW, Edmonton, AB T6G 1H9, Canada. jl11@ualberta.ca.
Jing
J
Li
Innovation Centre for Engineering, University of Alberta, 9211 116 Street NW, Edmonton, AB T6G 1H9, Canada. mahmoodi@ualberta.ca.
Alireza
A
Mahmoodi
Innovation Centre for Engineering, University of Alberta, 9211 116 Street NW, Edmonton, AB T6G 1H9, Canada. dil.joseph@ualberta.ca.
Dileepan
D
Joseph
Taylor series; fixed pattern noise; fixed-point arithmetic; logarithmic CMOS image sensor; look-up table; photometry; polynomial regression; spline interpolation