Abstract
We present a physics-informed deep learning framework to address common limitations in Confocal Laser Scanning Microscopy (CLSM), such as diffraction limited resolution, noise, and undersampling due to low laser power conditions. The optical system's point spread function (PSF) and common CLSM image degradation mechanisms namely photon shot noise, dark current noise, motion blur, speckle noise, and undersampling were modeled and were directly included into model architecture. The model reconstructs high fidelity images from heavily noisy inputs by using convolutional and transposed convolutional layers. Following the advances in compressed sensing, our approach significantly reduces data acquisition requirements without compromising image resolution. The proposed method was extensively evaluated on simulated CLSM images of diverse structures, including lipid droplets, neuronal networks, and fibrillar systems. Comparisons with traditional deconvolution algorithms such as Richardson-Lucy (RL), non-negative least squares (NNLS), and other methods like Total Variation (TV) regularization, Wiener filtering, and Wavelet denoising demonstrate the superiority of the network in restoring fine structural details with high fidelity. Assessment metrics like Structural Similarity Index (SSIM) and Peak Signal to Noise Ratio (PSNR), underlines that the AdaptivePhysicsAutoencoder achieved robust image enhancement across diverse CLSM conditions, helping faster acquisition, reduced photodamage, and reliable performance in low light and sparse sampling scenarios holding promise for applications in live cell imaging, dynamic biological studies, and high throughput material characterization.
Abstract (translated)
我们提出了一种基于物理信息的深度学习框架，旨在解决共聚焦激光扫描显微镜（CLSM）中常见的局限性问题，例如衍射限制分辨率、噪声以及由于低激光功率条件导致的采样不足。光学系统的点扩散函数（PSF）和CLSM图像退化机制——包括光子散粒噪声、暗电流噪声、运动模糊、颗粒噪声及采样不足等都被建模，并直接集成到模型架构中。该模型通过使用卷积层和转置卷积层，可以从高度噪点的输入数据重构出高保真度的图像。 借鉴压缩感知领域的进展，我们的方法显著减少了数据采集需求而不影响图像分辨率。所提出的方法在模拟CLSM图像上进行了广泛的评估，包括脂质滴、神经网络以及纤维系统等多样结构。与传统的反卷积算法如理查森-卢西（RL）、非负最小二乘法（NNLS）以及其他方法如总变差正则化、维纳滤波和小波去噪相比，证明了该网络在恢复精细结构细节方面具有更高的保真度。 评估指标如结构相似性指数（SSIM）和峰值信噪比（PSNR），突显出自适应物理自动编码器（AdaptivePhysicsAutoencoder）在各种CLSM条件下实现了稳健的图像增强。这有助于更快的数据采集、减少光损伤，并且在低光照及稀疏采样场景中提供可靠的性能，为活细胞成像、动态生物研究以及高通量材料表征等应用带来了希望。
URL
https://arxiv.org/abs/2501.14709
