Using Polynomials to Simplify Fixed Pattern Noise and Photometric Correction of Logarithmic CMOS Image Sensors

Sensors

Jing Li²  Alireza Mahmoodi²  Dileepan Joseph¹ 

[1] Innovation Centre for Engineering, University of Alberta, 9211 116 Street NW, Edmonton, AB T6G 1H9, Canada;

关键词: logarithmic CMOS image sensor;    fixed pattern noise;    photometry;    Taylor series;    polynomial regression;    spline interpolation;    fixed-point arithmetic;    look-up table;   

DOI  :  10.3390/s151026331

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.