High quality impulse noise removal via non‐uniform sampling and autoregressive modelling based super‐resolution is a scholarly article[1].
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High quality impulse noise removal via non‐uniform sampling and autoregressive modelling based super‐resolution's instance of is recorded as scholarly article[2].
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Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). High quality impulse noise removal via non‐uniform sampling and autoregressive modelling based super‐resolution. Retrieved May 24, 2026, from https://4ort.xyz/entity/high-quality-impulse-noise-removal-via-nonuniform-sampling-and-autoregressive-modelling-based-superresolution
MLA“High quality impulse noise removal via non‐uniform sampling and autoregressive modelling based super‐resolution.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/high-quality-impulse-noise-removal-via-nonuniform-sampling-and-autoregressive-modelling-based-superresolution.
BibTeX@misc{4ortxyz_high-quality-impulse-noise-removal-via-nonuniform-sampling-and-autoregressive-modelling-based-superresolution_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{High quality impulse noise removal via non‐uniform sampling and autoregressive modelling based super‐resolution}}, year = {2026}, url = {https://4ort.xyz/entity/high-quality-impulse-noise-removal-via-nonuniform-sampling-and-autoregressive-modelling-based-superresolution}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): High quality impulse noise removal via non‐uniform sampling and autoregressive modelling based super‐resolution — https://4ort.xyz/entity/high-quality-impulse-noise-removal-via-nonuniform-sampling-and-autoregressive-modelling-based-superresolution (retrieved 2026-05-24)
