Scene-based nonuniformity correction using sparse prior
The performance of infrared focal plane array (IRFPA) is known to be affected by the presence of spatial fixed pattern noise (FPN) that is superimposed on the true image. Scene-based nonuniformity correction (NUC) algorithms are widely concerned since they only need the readout infrared data captured by the imaging system during its normal operation. A novel adaptive NUC algorithm is proposed using the sparse prior that when derivative filters are applied to infrared images, the filter outputs tends to be sparse. A change detection module based on results of derivative filters is introduced to avoid stationary object being learned into the background, so the ghosting artifact is eliminated effectively. The performance of the new algorithm is evaluated with both real and simulated imagery.
nonuniformity correction sparse prior ghosting artifact
Xingang Mou Guilin Zhang Ruolan Hu Xiao Zhou
Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science andTec Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Te
国际会议
桂林
英文
1-7
2011-11-01（万方平台首次上网日期，不代表论文的发表时间）