面向无标记显微细胞图像增强的混合自适应多尺度感知驱动网络

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中图分类号：TP391.41 文献标志码：A 文章编号： 1000-5013（2026）02-0222-1：

Abstract：Aiming at the problems of the lack of standard paired data in label-free microscopic cell images，as well as the lowcontrast，insufficient brightnessand blurred cell edge details in labeled microscopic cellimages， a hybrid adaptive multi-scale perception-driven network is proposed for label-free microscopic cellimage enhancement.First，a hybrid adaptive Retinex module is designed to perform illumination optimization and structural preserving preprocessing for label-free microscopic cellimages.Second，a multi-scale illumination decomposition module is emplyed to separate high-frequency and low-frequency regions of the illumination component，obtaining illumination sensitivity coefficients and local noise levels of the image. Third，a dynamic Gamma parameter prediction module is constructed based on a lightweight CNN to generate a spatially variant Gamma correction mechanism，enabling pixel-level adaptive correction. Finally，an adaptive noise detection en

hancement module dynamically selects enhancement strategies according to the entropy value of the reflection component，effectively suppressing noise amplification. Experiments results show that the proposed method outperforms existing enhancement methods in terms of key quantitative metrics （NIQE，MV， IE），verifying its effectiveness in improving the visual quality of label-free microscopic cell images.

Keywords：adaptive Retinex；label-free microscopic cell image；image enhancement；illumination decomposi tion; noise suppression

无标记显微成像技术对生物医学研究至关重要，它无需外源标记即可保持细胞活性。（剩余17491字）