一种用于正交线扫描图像融合的统一分辨率条件框架
A Unified Resolution-Conditioned Framework for Orthogonal Line-Scanning Image Fusion
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中文总结 AI 辅助
针对正交线扫描图像融合，提出基于RELA和FiLM的统一分辨率条件框架，可跨狭缝宽度适配，性能优于现有方法，能平滑泛化到未见过的中间配置。
中文摘要 AI 辅助
激光线扫描显微镜可实现快速体积成像，但会产生各向异性的横向分辨率。正交线扫描提供互补的方向信息，可恢复近各向同性分辨率，然而现有的深度学习方法需要为每种光学配置单独训练一个模型。我们提出了一种基于秩增强线性注意力（Rank Enhanced Linear Attention, RELA）的统一分辨率条件融合框架。特征级线性调制（Feature-wise Linear Modulation, FiLM）使网络能根据分辨率比率进行连续条件调整，从而使单个模型可适应不同的狭缝宽度。我们进一步引入了自适应RELA，它用比率条件多尺度深度可分离卷积替代固定核的秩增强，并使用可学习的注意力温度根据退化严重程度调整选择性。我们使用基于物理的可分离点扩散函数模型生成涵盖多种狭缝配置的训练数据，该模型经实测光学数据验证，准确率达48.3 dB。所得模型在各配置下的峰值信噪比（PSNR）为34-40 dB，而无条件多狭缝训练的模型性能降至24.3 dB，专用单狭缝模型在其训练设置之外的性能则下降4-9 dB。该模型还能平滑泛化到未见过的中间配置，无插值伪影。消融实验表明，FiLM可解决配置歧义，全局线性注意力能捕捉长程方向对应关系，而自适应温度在互补信号较弱的具有挑战性的近各向同性区域可额外提升2 dB性能。
英文摘要
Laser line-scanning microscopy enables fast volumetric imaging but produces anisotropic lateral resolution. Orthogonal line scans provide complementary directional information that can recover near-isotropic resolution, yet existing deep-learning methods require a separate model for each optical configuration. We present a unified, resolution-conditioned fusion framework based on Rank Enhanced Linear Attention (RELA). Feature-wise Linear Modulation (FiLM) conditions the network continuously on the resolving-power ratio, enabling one model to adapt across slit widths. We further introduce Adaptive RELA, which replaces fixed-kernel rank enhancement with ratio-conditioned multi-scale depthwise convolutions and uses a learnable attention temperature to adjust selectivity with degradation severity. Training data spanning multiple slit configurations are generated using a physics-grounded separable point-spread-function model verified against measured optical data at 48.3 dB accuracy. The resulting model achieves 34-40 dB PSNR across configurations, whereas unconditioned multi-slit training collapses to 24.3 dB and per-slit specialists lose 4-9 dB outside their training setting. It also generalizes smoothly to unseen intermediate configurations without interpolation artifacts. Ablations show that FiLM resolves configuration ambiguity, global linear attention captures long-range directional correspondences, and adaptive temperature yields an additional 2 dB in the challenging near-isotropic regime, where complementary signals are weak.
