  Photometric surveys have provided incredible amounts of astronomical
information in the form of images. However, astronomical images often contain
artifacts that can critically hinder scientific analysis by misrepresenting
intensities or contaminating catalogs as artificial objects. These affected
pixels need to be masked and dealt with in any data reduction pipeline. In this
paper, we present a flexible, iterative algorithm to recover (unmask)
astronomical images where some pixels are lacking. We demonstrate the
application of the method on some intensity calibration source images in CO
Multi-line Imaging of Nearby Galaxies (COMING) Project conducted using the 45m
telescope at Nobeyama Radio Observatory (NRO). The proposed algorithm restored
artifacts due to a detector error in the intensity calibration source images.
The restored images were used to calibrate 11 out of 147 observed galaxy maps
in the survey. The tests show that the algorithm can restore measured
intensities at sub 1% error even for noisy images (SNR = 2.4), despite lacking
a significant part of the image. We present the formulation of the
reconstruction algorithm, discuss its possibilities and limitations for
extensions to other astronomical signals and the results of the COMING
application.
