KEYWORDS: Image restoration, Education and training, X-ray imaging, Deep learning, X-rays, Reconstruction algorithms, Image quality, Data modeling, Point spread functions, Algorithm development
A novel method for image restoration is introduced that uses a synthetic prior intermediate (SPI) which is passed through a forward imaging operator, creating a data pair well-structured for inverse operator optimization, of which network training is of particular interest. This technique is applied to a critical problem in x-ray reconstruction: noise and artefact removal. We discuss the creation of the SPI through state-of-the-art Deep Learning Reconstruction (DLR), a spatially variant heuristic data-driven forward model for spectrally accurate noise and artefact modelling, and final image reconstruction via a convolutional neural network. Qualitative and quantitative performance is then benchmarked on a range of samples, comparing legacy reconstruction (FDK), state-of-the-art DLR, and SPI based reconstruction. SPI based reconstruction better recovers small features while also reducing residual sampling artefacts in large features. Quantitative analysis of SPI reconstruction showed a 40% throughput improvement relative to the state-of-the-art at a comparable image quality.
X-ray computed laminographic tomography (CLT) is a viable tool for creating high-throughput volumetric imaging of large, planar samples. In this work, we present a self-supervised deep image restoration workflow to produce noise-free, artifact-free volumetric reconstructions for laminographic tomography. We demonstrate our CLT method on a variety of samples scanned with an in-house prototype system, showing that our proposed method notably outperforms classic reconstruction methods, that has the potential for more accurate detection of defects and estimation of critical dimensions, thereby providing a feasible solution for rapid inline inspection and failure analysis in advanced integrated circuits packaging.
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