With advancements on synchrotron radiation instrumentations particularly the emergence of next-generation light sources with ultra-low emittance and high intensity, scientific experiments have shifted from 2D static to multidimensional dynamic characterizations with multimodal approaches, enabling new capabilities of probing structural and functional changes across multiple length scales with extreme spatial and temporal resolution. However, such advancement on synchrotron hardware imposes tremendous challenges on the data processing end. Effective software tools and algorithms addressing image denoising, segmentation and data compression problems can be of great use to alleviate the burden, fully releasing the power of advanced instrumentations. Instead of separately utilizing modular image processing algorithms, this review suggests a unified experimental control and data acquisition software framework that naturally interfaces and integrates advanced software and algorithms with extensive AI assistance would be best suitable for next-generation beamlines. The review will also discuss prospective applications artificial intelligence and large-scale high performance computing infrastructures could bring about to facilitate scientific discoveries at next-generation synchrotron light sources.
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