Plug-and-play computational method for advancing natural and biomedical image representation.
basic_science · Level V
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- Record sourced from PubMed, PMID 41328168.
- Also identified by DOI 10.1016/j.patter.2025.101412 and PMC identifier 12664947.
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Abstract
Visual information is central to human perception, and how we represent images critically shapes downstream analysis. While recent years have witnessed remarkable advances in deep learning for image processing, Valous et al. now introduce a non-data-driven framework grounded in hypercomplex algebras for natural and biomedical image processing. This plug-and-play approach operates without training data and shows its effect across tasks such as re-colorization, de-colorization, contrast enhancement, re-staining, and integration into machine-learning pipelines.