Detection and restoration of abnormal band data in photometric images

Addressing the issue of abnormal band data processing in photometric surveys is imperative. Restoring of abnormal band data not only salvages a significant amount of existing astronomical observation data, but also has profound implications on the data processing of new optical telescopes in the future. This paper firstly designs Band Data MogaNet(BDMogaNet) classification model for normal or abnormal band data, which can automatically identify abnormal data. Then, for the restoration of abnormal band data, Global–Local Recursive Generalization(GLRG) restoration network is designed. The experiment used the SDSS image library, and the results proved that the classification accuracy of normal band data and abnormal band data using BDMogaNet reached 99.2% in the training set and 98.0% in the validation set, which had a better classification comparing to some newest methods. Moreover, PSNR of restoring abnormal band data using GLRG reached 33.96 dB, SSIM reached 0.73, and CM reached 6.09, which are all better compared to some newest methods.