2nd. International Conference on Applied and Theoretical Information Systems Research, December 27-29, 2012, Taipei, Taiwan 1 A HIGH PERFORMANCE MISSING PIXEL RECONSTRUCTION ALGORITHM FOR HYPERSPECTRAL IMAGES Jin Zhou, Signal Processing, Inc., USA jin.zhou@signalpro.net Chiman Kwan, Signal Processing, Inc., USA chiman.kwan@signalpro.net Bulent Ayhan Signal Processing, Inc., USA bulent.ayhan@signalpro.net ABSTRACT In many image processing applications, pixels may be corrupted or simply missing. In some other cases, pixels may be randomly deleted in order to save bandwidth during transmission. It is important to develop high performance algorithms that can reconstruct those corrupted or missing pixels. In this paper, we will summarize our research effort in developing a high performance reconstruction algorithm for reconstructing missing pixels in hyperspectral images. Experiments using actual images clearly demonstrated that our algorithm can achieve high reconstruction performance even in high missing rates as high as 95% or 99%. Keywords: Hyperspectral images, missing data reconstruction, matrix completion 1. INTRODUCTION 1.1 Data with Missing Information In social networks, links between subjects may be missing. In some images, pixels may be corrupted or simply missing. In some surveillance applications, pixels may be randomly deleted to save network bandwidth. For example, a low cost unmanned air vehicle running surveillance operations may not have enough onboard computational power to perform sophisticated image compression. A simple way to save bandwidth is to randomly select some pixels and send them over to the ground station, which will then reconstruct the missing pixels using high performance computers. 1.2 Matrix Completion In all of the above mentioned scenarios, the missing data locations are known. Conventional methods use interpolation from neighboring pixels to fill in the missing data. However, interpolation can only utilize local information. If the original data have