We present an image sensor noise model that can be used in a complete image system simulation that includes image generation, lens degradations, and ISP (Image Signal Processing), and can produce classic measurements (SFR, noise, etc.) as well as new information metrics such as information capacity and SNRi. The noise model is derived from a classic Photon Transfer Curve (PTC) obtained from one or more raw (undemosaiced) images of a high dynamic range grayscale test chart. Image sensor noise is composed of three factors. 1. Dark noise, which includes electronic noise, dark current noise, DSNU fixed-pattern noise, and noise from several other sources. It is independent of signal amplitude, A. 2. Photon shot noise, which varies with √𝐴, and 3. PRNU fixed-pattern noise, which varies linearly with A. The coefficients for the three factors are determined using a Levenberg Marquardt optimizer that provides an extremely close fit between the measured data and the calculated PTC. The coefficients can also be derived from EMVA 1288 measurements, which are more accurate and detailed, but require the acquisition of a large number of images. We show how the model can predict performance over a wide range of conditions, and most importantly, for low light.