用于可扩展空间变化高通量成像的坐标条件去卷积
Coordinate-conditioned Deconvolution for Scalable Spatially Varying High-Throughput Imaging
AI总结：
SV-CoDe通过坐标条件卷积实现空间变化高通量成像的可扩展高分辨率重建，减少模型大小和训练数据需求，适用于多种生物医学成像应用。
摘要：
宽场荧光显微镜通常因场依赖性像差、光晕效应和传感器截断而产生空间变化的模糊，同时有限的传感器采样会带来视野（FOV）和分辨率之间的固有权衡。计算微型化mesoscope（CM2）通过将多个子视图复用到单个传感器上缓解了采样限制，但引入了视图串扰和由空间变化点扩散函数（PSFs）加剧的高逆问题。先前基于学习的空间变化（SV）重建方法通常依赖于全局SV算子，具有固定输入尺寸，导致内存和训练成本随图像尺寸增长而恶化。我们提出SV-CoDe（空间变化坐标条件去卷积），一种可扩展的深度学习框架，能够在6.5毫米FOV上实现均匀的高分辨率重建。与传统方法不同，SV-CoDe采用坐标条件卷积来局部适应重建内核；这使得基于补丁的训练将参数数量与FOV大小解耦。SV-CoDe在模拟和实验测量中均实现了最佳图像质量，同时模型大小和训练数据需求比先前基线减少了10倍。仅在基于物理的模拟上训练的网络能够稳健地推广到珠状假体、弱散射脑切片和自由移动的C. elegans。SV-CoDe为纠正紧凑光学系统中的空间变化模糊提供了可扩展、物理感知的解决方案，并可广泛应用于生物医学成像领域。
英文摘要：
Wide-field fluorescence microscopy with compact optics often suffers from spatially varying blur due to field-dependent aberrations, vignetting, and sensor truncation, while finite sensor sampling imposes an inherent trade-off between field of view (FOV) and resolution. Computational Miniaturized Mesoscope (CM2) alleviate the sampling limit by multiplexing multiple sub-views onto a single sensor, but introduce view crosstalk and a highly ill-conditioned inverse problem compounded by spatially variant point spread functions (PSFs). Prior learning-based spatially varying (SV) reconstruction methods typically rely on global SV operators with fixed input sizes, resulting in memory and training costs that scale poorly with image dimensions. We propose SV-CoDe (Spatially Varying Coordinate-conditioned Deconvolution), a scalable deep learning framework that achieves uniform, high-resolution reconstruction across a 6.5 mm FOV. Unlike conventional methods, SV-CoDe employs coordinate-conditioned convolutions to locally adapt reconstruction kernels; this enables patch-based training that decouples parameter count from FOV size. SV-CoDe achieves the best image quality in both simulated and experimental measurements while requiring 10x less model size and 10x less training data than prior baselines. Trained purely on physics-based simulations, the network robustly generalizes to bead phantoms, weakly scattering brain slices, and freely moving C. elegans. SV-CoDe offers a scalable, physics-aware solution for correcting SV blur in compact optical systems and is readily extendable to a broad range of biomedical imaging applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
