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Radiometric Calibration by Rank Minimization
Jan. 2013 (vol. 35 no. 1)
pp. 144-156
Joon-Young Lee, Dept. of Electr. Eng., KAIST, Daejeon, South Korea
Y. Matsushita, Microsoft Res. Asia, Beijing, China
Boxin Shi, Ikeuchi Lab., Univ. of Tokyo, Tokyo, Japan
In So Kweon, Dept. of Electr. Eng., KAIST, Daejeon, South Korea
K. Ikeuchi, Ikeuchi Lab., Univ. of Tokyo, Tokyo, Japan
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.
Index Terms:
radiometry,calibration,image sensors,minimisation,real-world datasets,rank minimization,robust radiometric calibration framework,transform invariant low-rank structure,sensor irradiances,static scene,exposure times,linear structure,irradiance color mixtures,radiometric calibration problems,principled framework,Image color analysis,Radiometry,Calibration,Noise,Vectors,Cameras,Minimization,low-rank structure,Radiometric calibration,camera response function,rank minimization
Citation:
Joon-Young Lee, Y. Matsushita, Boxin Shi, In So Kweon, K. Ikeuchi, "Radiometric Calibration by Rank Minimization," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 1, pp. 144-156, Jan. 2013, doi:10.1109/TPAMI.2012.66
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