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Spectral super-resolution is critical in transforming multispectral images into hyperspectral variants. Its profound importance is evident, yet its adoption in medical imaging reveals a palpable gap. Historically, many networks rely on correlation for grouping spectral bands within the visible light spectrum. However, several medical case images are enriched with information in the near-infrared spectrum, mainly attributed to the near-infrared&#x2019;s ability to penetrate the cellular surface, thereby accessing deeper layers of information. Therefore, grouping from a new perspective is very important. To bridge this gap, we introduce a Spatial-attention Transformer In Spectral-probability Transformer Network (TNT++), explicitly designed to enhance the spectral super-resolution of medical imagery. This methodology is tailored uniquely, drawing upon the inherent pixel statistical properties typical of medical hyperspectral images. Notably, by calculating the entropy value based on the pixel distribution of individual spectral bands, we unveiled the inherent joint spectral entropy patterns in the dataset, introducing an entropy-based grouping and revealing the nuances in image disorder levels&#x2014;subtleties previously neglected. Our revamped transformer exhibits superior adaptability, proficiently capturing both the spatial and spectral complexities while adeptly navigating the intricacies of image architectures. Rigorous evaluations on the open-source Multidimensional Liver Cancer pathology dataset validate our model&#x2019;s excellence. Outshining six contemporary state-of-the-art (SOTA) techniques across four established metrics, it achieves a PSNR of 31.95db and an SSIM of 0.9065, marking a significant stride forward in this discipline.

TNT++: A Spectral Super-Resolution Method Based on the Entropy of Pathological Images