Cross-talk correction methodology for color CMOS imagers

Abstract

A pixel signal cross-talk correction method that utilizes knowledge of a color image sensor's performance characteristics is presented. The objective is to create a simple, non-iterative algorithm that can be implemented in the on-chip digital logic of an imaging sensor. Inverse cross-talk Bayer color filter array pattern filters are determined for the blurring, multi-channel, cross-channel problem. Simple noise and cross-talk models are developed and used to solve for the corrective deconvolution filters. The noise statistics used have both signal independent and dependent components, and include the noise associated with cross-talk. The methodology is independent of image statistics. The inverse filters are found by solving a set of simultaneous linear equations in the discrete Fourier frequency domain. A direct deterministic regularization method with constrained least squares is then used to solve the ill-posed problem. The local pixel blurred signal-to-noise ratio is used as the regularization parameter. This yields an inverse blur filter weighted by a local scalar noise filter. The resulting method provides a locally adaptive trade-off between cross-talk correction and noise smoothing. Algorithm performance is compared with the standard 3x3 matrix color correction method for image mean square error, color error, flat SNR, and modulation transfer function.