The ionization profile monitor (IPM) can provide critical beam distribution information required for real-time debugging and stable operation of high-current proton accelerators. The IPM system of the China Spallation Neutron Source (CSNS) Linac adopts a compact structural design. It collects data in ion mode and performs one-dimensional transverse beam distribution measurement through an optical imaging system. However, the honeycomb mesh structure at the electrode plate apertures blocks some ions or electrons from entering the microchannel plate, causing imaging shadows and introducing beam distribution distortion. Offline numerical algorithms must be used for correction. In this paper, partial differential equation (PDE) restoration and machine learning algorithms are used to correct the imaging shadows and beam distribution distortion caused by the honeycomb mesh of the IPM in the CSNS linac. The unsupervised machine learning method DIP (Deep Image Prior) was employed, and the corrected beam size deviates from the theoretical expectation by less than 10%, while maintaining a good signal-to-noise ratio.