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.
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