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Achieving High-Fidelity and Consistent Super-Resolution in Pathological Images using a Novel Consistency-Restricted Diffusion Framework
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DOI：10.1007/s11263-026-02972-3.png)
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Abstract
En 中文
High-quality digital pathological images face challenges such as high acquisition costs and difficulties in storage and transmission owing to their considerably large size. Several studies based on single-image super-resolution have been conducted to address these challenges; however, the methods employed in these studies generally exhibit excessive smoothness and optimization instability, failing to generate high-fidelity images. This study presents consistency-restricted pathological image reconstruction (CorPiR), an effective two-stage framework for super-resolution pathological image reconstruction. CorPiR uses a lightweight regression-based method in the first stage to quickly reconstruct low-resolution inputs. In the second stage, a consistency-restricted diffusion (CorD) model is proposed to reconstruct image details to generate high-fidelity pathological images. By introducing a cross-attention mechanism-based module, consistency constrainer (CConstrainer), the CorD model enhances the consistency between generated and input images without compromising visual fidelity. Furthermore, we designed a consistency evaluator module to extract consistency prior information from a low-resolution input image, guiding CConstrainer to focus on reconstructing regions that lack consistency. Extensive experiments on a pan-cancer dataset, with ten cancer types at four different magnification levels, verify the effectiveness of the CorPiR framework, demonstrating superior performance compared to existing state-of-the-art methods qualitatively and quantitatively. In addition, evaluations performed by pathologists and the results from handcrafted texture feature experiments demonstrated that CorPiR could generate images with both high visual fidelity and consistency, providing valuable support for clinical diagnosis and downstream computational tasks. The CorPiR code is publicly available at https://github.com/ZHT150798/CorPiR .
Keywords:
Pathological images
Super-resolution
Diffusion model
Cross-attention mechanism
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