Lightweight deep learning models offer substantial reductions in computational cost and environmental impact, making them crucial for scientific applications. We present a lightweight CycleGAN for modality transfer in fluorescence microscopy (confocal to super-resolution STED/deconvolved STED), addressing the common challenge of unpaired datasets. By replacing the traditional channel-doubling strategy in the U-Net-based generator with a fixed channel approach, we drastically reduce trainable parameters from 41.8 million to approximately nine thousand, achieving superior performance with faster training and lower memory usage. We also introduce the GAN as a diagnostic tool for experimental and labeling quality. When trained on high-quality images, the GAN learns the characteristics of optimal imaging; deviations between its generated outputs and new experimental images can reveal issues such as photobleaching, artifacts, or inaccurate labeling. This establishes the model as a practical tool for validating experimental accuracy and image fidelity in microscopy workflows.

翻译：轻量化深度学习模型能显著降低计算成本和环境影响，这对科学应用至关重要。我们提出了一种用于荧光显微镜模态转换（共聚焦到超分辨率STED/去卷积STED）的轻量化CycleGAN，以解决非配对数据集的常见挑战。通过将基于U-Net的生成器中传统的通道倍增策略替换为固定通道方法，我们将可训练参数从4180万大幅减少至约九千个，实现了更快的训练速度、更低的内存占用以及更优的性能。我们还引入GAN作为实验与标记质量的诊断工具。当使用高质量图像训练时，GAN能学习到最佳成像的特征；其生成输出与新实验图像之间的偏差可以揭示光漂白、伪影或标记不准确等问题。这使该模型成为显微镜工作流程中验证实验准确性与图像保真度的实用工具。