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CT kernel conversions using convolutional neural net for super-resolution with simplified squeeze-and-excitation blocks and progressive learning among smooth and sharp kernels
Research article (Computer Methods and Programs in Biomedicine, 2020) · cited 14× · AI/ML
CT kernel conversions using convolutional neural net for super-resolution with simplified squeeze-and-excitation blocks and progressive learning among smooth and sharp kernels
Summary
CT kernel conversions using convolutional neural net for super-resolution with simplified squeeze-and-excitation blocks and progressive learning among smooth and sharp kernels is a scholarly article[1].
Key Facts
CT kernel conversions using convolutional neural net for super-resolution with simplified squeeze-and-excitation blocks and progressive learning among smooth and sharp kernels's instance of is recorded as scholarly article[2].
References
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APA4ort.xyz Knowledge Graph. (2026). CT kernel conversions using convolutional neural net for super-resolution with simplified squeeze-and-excitation blocks and progressive learning among smooth and sharp kernels. Retrieved May 24, 2026, from https://4ort.xyz/entity/ct-kernel-conversions-using-convolutional-neural-net-for-super-resolution-with-simplified-squeeze-and-excitation-blocks-
MLA“CT kernel conversions using convolutional neural net for super-resolution with simplified squeeze-and-excitation blocks and progressive learning among smooth and sharp kernels.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/ct-kernel-conversions-using-convolutional-neural-net-for-super-resolution-with-simplified-squeeze-and-excitation-blocks-.
BibTeX@misc{4ortxyz_ct-kernel-conversions-using-convolutional-neural-net-for-super-resolution-with-simplified-squeeze-and-excitation-blocks-_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{CT kernel conversions using convolutional neural net for super-resolution with simplified squeeze-and-excitation blocks and progressive learning among smooth and sharp kernels}}, year = {2026}, url = {https://4ort.xyz/entity/ct-kernel-conversions-using-convolutional-neural-net-for-super-resolution-with-simplified-squeeze-and-excitation-blocks-}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): CT kernel conversions using convolutional neural net for super-resolution with simplified squeeze-and-excitation blocks and progressive learning among smooth and sharp kernels — https://4ort.xyz/entity/ct-kernel-conversions-using-convolutional-neural-net-for-super-resolution-with-simplified-squeeze-and-excitation-blocks- (retrieved 2026-05-24)
