Compensating Projection Interpolation Artifacts in Sparse-View CT using a Two- Stage CNN with learnable Upscaling

X-ray Computed Tomography (CT) is a valuable tool for non-destructive testing, typically requiring thousands of projections to reconstruct high-quality images. For inline CT applications, however, scan time constraints necessitate reducing either the tube current or the number of projections. These limitations introduce significant noise and streak artifacts in reconstructed images. Building on existing approaches for artifact compensation, we propose a novel method to address missing projections through learned angular upscaling of sparse-view sinograms. Our approach modifies the standard U-Net architecture by integrating additional convolution and pixel-shuffle layers to recover missing data. During training, the upscaling kernel parameters are optimized to maximize alignment with ground-truth projections through three training phases. Even at an extreme sparsity factor of 20× fewer projections, our method demonstrates substantial improvement over interpolation-based upscaling deep learning techniques and achieves significant enhancement of sparse-view reconstructions for experimental results.