CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2013 vol.35 Issue No.01 - Jan.
Issue No.01 - Jan. (2013 vol.35)
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
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2012.66
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.
Image color analysis, Radiometry, Calibration, Noise, Vectors, Cameras, Minimization,low-rank structure, Radiometric calibration, camera response function, rank minimization
Joon-Young Lee, Y. Matsushita, Boxin Shi, In So Kweon, K. Ikeuchi, "Radiometric Calibration by Rank Minimization", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.35, no. 1, pp. 144-156, Jan. 2013, doi:10.1109/TPAMI.2012.66