Many computer vision algorithms need to measure the scene radiance accurately, and the camera response function can achieve this result by establishing the mapping between image brightness and scene radiance. Camera response function calibration is the key to high dynamic range image fusion. The properties that all camera response functions share were analyzed in the paper, which helps us to find the constraints that any camera response function must satisfy and establish the theoretical space model of camera response function. Firstly, the database of real-world camera response functions was analyzed by principal component analysis algorithm and low-parameter empirical model of response was established combined with constraints; secondly, appropriate parameter number was chosen according to input images; finally, the coefficients to the low-parameter empirical model of camera response function was solved by least square method. The algorithm proposed in this paper could establish camera response function of the imaging system accurately by interpolating to sparse samples or multiple images with different exposures in arbitrary environment. The effectiveness of this camera response function calibration algorithm was verified by different experiments, which proved high-accuracy and high computational efficiency of this algorithm.