Images recorded by digital cameras are invariably distorted by errors of CCD sensors and a series of camera operations in the imaging process. The distortion sources include noise, geometrical distortion, gamma correction, intensity and chromatic bias, blurring, etc. However, the true signal of the incident light needs to be known in many situations. In addition to traditional visual computing tasks such as shape from shading, color constancy, and photometric stereo, acquiring a large natural image database in which each image has a high-dynamic range and is carefully calibrated to reflect the true signal of the incident light is essential to human vision research. We recently acquired such a database containing 1600 images using an Olympus C2040 digital camera and explained a series of color perceptual phenomena based on the statistics of these images. We introduced in this paper the techniques we used for calibrating and acquiring this database, which include the methods to correct the spatial falloff, non-linearity, spectral bias, blurring, and noises and to obtain high-dynamic range for each image. The techniques presented here can be used in acquiring similar databases for a wide range of human vision and computer vision research fields.