CAMERA SPECTRAL SENSITIVITY CHARACTERIZATION USING A BLACKBODY SOURCE

With digital cameras emerging as more effective tools for scientific research, there is increasing need for accurate and inexpensive ways to calibrate them. In particular, to date there has been no simple method to measure camera sensitivity as a function of wavelength. For example, narrow bandwidth monochromator beams are expensive and have calibration problems, while color chart method is unreliable owing to illumination dependence. This thesis presents a novel technique for spectral sensitivity calibration of a camera (or any black-and-white cameras or color sensors) using blackbody furnace operating at 650 - 1250 °C. Images recorded at 11 different temperatures are observed for red, green, and blue camera outputs. Using Planck ’ s Law to calculate the incident light intensities, the three color sensitivities as functions of wavelength are computed using MATLAB function that optimizes the spectral sensitivities until the blackbody measurements are closely matched. The results are in reasonable agreement with published sensitivities

Recovering Spectral Sensitivities with Uncertainty

It is well established that in order to obtain the best colour performance of a colour input device such as a scanner or a camera,that one needs to know the device spectral sensitivities.Unfortunately measuring sensitivities outside the laboratory is hard and moreover,manufacturers are reluctant to give the user specifications.Thus,there has been considerable interest in developing numerical techniques for estimating the spectral sensitivities. These methods are based on taking images of known spectral targets and then,using knowledge of the image formation process,solving for the sensitivities using numerical methods.It is important to state that while these methods perform reasonably well,the problem is inherently ill-posed.There is simply not enough degrees of freedom in the spectral profile of a reflectance target to recover device sensitivities. In this paper we tackle this uncertainty head on and develop a method to recover device sensitivities with uncertainty error bars.Experiments with a Megavision camera return a sensor estimate together with error bars. The error bars are sufficient to explain the discrepancy in the recoveries delivered by single-answer estimation algorithms and the actual sensitivities