We present a robust radiometric calibration framework that capitalizes on the transform invariant low-rank structure in the various types of observations, such as sensor irradiances recorded from a static scene with different exposure times, or linear structure of irradiance color mixtures around edges. We show that various radiometric calibration problems can be treated in a principled framework that uses a rank minimization approach. This framework provides a principled way of solving radiometric calibration problems in various settings. The proposed approach is evaluated using both simulation and real-world datasets and shows superior performance to previous approaches.
|저 자||Joon-Young Lee, Yasuyuki Matsushita, Boxin Shi, In So Kweon, Katsushi Ikeuchi|
|학 회||IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)|