We present a robust radiometric calibration method that capitalizes on the transform invariant low-rank structure of sensor irradiances recorded from a static scene with different exposure times. We formulate the radiometric calibration problem as a rank minimization problem. Unlike previous approaches, our method naturally avoids over-fitting problem; therefore, it is robust against biased distribution of the input data, which is common in practice. When the exposure times are completely unknown, the proposed method can robustly estimate the response function up to an exponential ambiguity. The method is evaluated using both simulation and real-world datasets and shows a superior performance than previous approaches.
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|Joon-Young Lee, Boxin Shi, Yasuyuki Matsushita, In So Kweon, Katsushi Ikeuchi
|IEEE Conference on Computer Vision and Pattern Recognition (CVPR)