Camera spectral sensitivity functions relate scene radiance with captured RGB triplets. They are important for many computer vision tasks that use color information, such as multispectral imaging, color rendering, and color constancy. In this paper, we aim to explore the space of spectral sensitivity functions for digital color cameras. After collecting a database of 28 cameras covering a variety of types, we find this space convex and two-dimensional. Based on this statistical model, we propose two methods to recover camera spectral sensitivities using regular reflective color targets (e.g., color checker) from a single image with and without knowing the illumination. We show the proposed model is more accurate and robust for estimating camera spectral sensitivities than other basis functions. We also show two applications for the recovery of camera spectral sensitivities -simulation of color rendering for cameras and computational color constancy.

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Recovering the spectral reflectance of a scene is important for scene understanding. Previous approaches use either specialized filters or controlled illumination where the extra hardware prevents many practical applications. In this paper, we propose a method that accurately recovers spectral reflectance from two images taken with conventional consumer cameras under commonly available lighting conditions, such as daylight at different times over a day, camera flash and ambient light, and fluorescent and tungsten light. Our approach does not require camera spectral sensitivities or the spectra of the illumination, which makes it easy to implement for a variety of practical applications. Based on noise analysis, we also derive theoretical predictors that answer: (1) which two lighting conditions lead to the most accurate spectral recovery overall, and (2) for two given lighting conditions, which spectral reflectance is more likely to be estimated accurately. We implement the method on a variety of cameras from high-end DSLRs to cellphone cameras, and apply the recovered spectral reflectance for several applications such as fine art reproduction, fruit identification, and material classification. Both simulation and experimental results demonstrate the effectiveness of the proposed method.

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