arXiv subjects

Renzhi He

Publications and source records attributed to Renzhi He.

7 recordsLinked to original sources

arXiv2026

Intensity-based scattering correction enables in vivo two-photon imaging beyond 1 mm

Optical imaging of the deep brain with subcellular resolution is essential for neuroscience, but noninvasive imaging beyond the cortex, through scattering white matter and into the hippocampus, has generally required three-photon microscopy at longer excitation wavelengths. Here, we introduce deep-learning-enhanced Fourier-domain intensity coupling for scattering correction (DeepFOCUS), an intensity-based two-photon approach that uses deep learning to compute intensity-modulation masks for real-time modulation of excitation light during image acquisition. Unlike deep-learning-based image restoration, this method directly improves image formation by computing intensity-modulation masks that shape the excitation light in real time, with each mask experimentally validated by the acquired fluorescence signal to avoid hallucination artifacts. Using 1035 nm excitation, we achieved in vivo two-photon imaging beyond 1 mm depth in the intact mouse brain, resolving YFP-labeled neurons and FITC-labeled blood vessels through the entire cortex and white matter down to the CA1 region of the hippocampus. DeepFOCUS extends two-photon imaging to depths previously accessible mainly with three-photon microscopy and could enable broader adoption of hippocampal imaging by upgrading existing two-photon systems.

physics.optics↗

arXiv2025

Recover Biological Structure from Sparse-View Diffraction Images with Neural Volumetric Prior

Volumetric reconstruction of label-free living cells from non-destructive optical microscopic images reveals cellular metabolism in native environments. However, current optical tomography techniques require hundreds of 2D images to reconstruct a 3D volume, hindering them from intravital imaging of biological samples undergoing rapid dynamics. This poses the challenge of reconstructing the entire volume of semi-transparent biological samples from sparse views due to the restricted viewing angles of microscopes and the limited number of measurements. In this work, we develop Neural Volumetric Prior (NVP) for high-fidelity volumetric reconstruction of semi-transparent biological samples from sparse-view microscopic images. NVP integrates explicit and implicit neural representations and incorporates the physical prior of diffractive optics. We validate NVP on both simulated data and experimentally captured microscopic images. Compared to previous methods, NVP significantly reduces the required number of images by nearly 50-fold and processing time by 3-fold while maintaining state-of-the-art performance. NVP is the first technique to enable volumetric reconstruction of label-free biological samples from sparse-view microscopic images, paving the way for real-time 3D imaging of dynamically changing biological samples. \href{https://xue-lab-cobi.github.io/Sparse-View-FDT/}{Project Page}

physics.optics↗

arXiv2025

Compressive Fourier-Domain Intensity Coupling (C-FOCUS) enables near-millimeter deep imaging in the intact mouse brain in vivo

Two-photon microscopy is a powerful tool for in vivo imaging, but its imaging depth is typically limited to a few hundred microns due to tissue scattering, even with existing scattering correction techniques. Moreover, most active scattering correction methods are restricted to small regions by the optical memory effect. Here, we introduce compressive Fourier-domain intensity coupling for scattering correction (C-FOCUS), an active scattering correction approach that integrates Fourier-domain intensity modulation with compressive sensing for two-photon microscopy. Using C-FOCUS, we demonstrate high-resolution imaging of YFP-labeled neurons and FITC-labeled blood vessels at depths exceeding 900 um in the intact mouse brain in vivo. Furthermore, we achieve transcranial imaging of YFP-labeled dendritic structures through the intact adult mouse skull. C-FOCUS enables high-contrast fluorescence imaging at depths previously inaccessible using two-photon microscopy with 1035 nm excitation, enhancing fluorescence intensity by over 20-fold compared to uncorrected imaging. C-FOCUS provides a broadly applicable strategy for rapid, deep-tissue optical imaging in vivo.

physics.optics↗

arXiv2025

Code Improvement Practices at Meta

The focus on rapid software delivery inevitably results in the accumulation of technical debt, which, in turn, affects quality and slows future development. Yet, companies with a long history of rapid delivery exist. Our primary aim is to discover how such companies manage to keep their codebases maintainable. Method: we investigate Meta's practices by collaborating with engineers on code quality and by analyzing rich source code change history to reveal a range of practices used for continual improvement of the codebase. In addition, we replicate several aspects of previous industry cases studies investigating the impact of code reengineering. Results: Code improvements at Meta range from completely organic grass-roots done at the initiative of individual engineers, to regularly blocked time and engagement via gamification of Better Engineering (BE) work, to major explicit initiatives aimed at reengineering the complex parts of the codebase or deleting accumulations of dead code. Over 14% of changes are explicitly devoted to code improvement and the developers are given ``badges'' to acknowledge the type of work and the amount of effort. Our investigation to prioritize which parts of the codebase to improve lead to the development of metrics to guide this decision making. Our analysis of the impact of reengineering activities revealed substantial improvements in quality and speed as well as a reduction in code complexity. Overall, such continual improvement is an effective way to develop software with rapid releases, while maintaining high quality.

cs.SE↗

arXiv2024

Fluorescence Diffraction Tomography using Explicit Neural Fields

Simultaneous imaging of fluorescence-labeled and label-free phase objects in the same sample provides distinct and complementary information. Most multimodal fluorescence-phase imaging operates in transmission mode, capturing fluorescence images and phase images separately or sequentially, which limits their practical application in vivo. Here, we develop fluorescence diffraction tomography (FDT) with explicit neural fields to reconstruct the 3D refractive index (RI) of phase objects from diffracted fluorescence images captured in reflection mode. The successful reconstruction of 3D RI using FDT relies on four key components: a coarse-to-fine structure, self-calibration, a differential multi-slice rendering model, and partially coherent masks. The explicit representation integrates with the coarse-to-fine structure for high-speed, high-resolution reconstruction, while the differential multi-slice rendering model enables self-calibration of fluorescence illumination, ensuring accurate forward image prediction and RI reconstruction. Partially coherent masks efficiently resolve discrepancies between the coherent light model and partially coherent light data. FDT successfully reconstructs the RI of 3D cultured label-free bovine myotubes in a 530 $\times$ 530 $\times$ 300 $μm^3$ volume at 1024 $\times$ 1024 pixels across 24 $z$-layers from fluorescence images, demonstrating high resolution and high accuracy 3D RI reconstruction of bulky and heterogeneous biological samples in vitro.

physics.optics↗

arXiv2022

Point Spread Function Estimation of Defocus

This Point spread function (PSF) plays a crucial role in many computational imaging applications, such as shape from focus/defocus, depth estimation, and fluorescence microscopy. However, the mathematical model of the defocus process is still unclear. In this work, we develop an alternative method to estimate the precise mathematical model of the point spread function to describe the defocus process. We first derive the mathematical algorithm for the PSF which is used to generate the simulated focused images for different focus depth. Then we compute the loss function of the similarity between the simulated focused images and real focused images where we design a novel and efficient metric based on the defocus histogram to evaluate the difference between the focused images. After we solve the minimum value of the loss function, it means we find the optimal parameters for the PSF. We also construct a hardware system consisting of a focusing system and a structured light system to acquire the all-in-focus image, the focused image with corresponding focus depth, and the depth map in the same view. The three types of images, as a dataset, are used to obtain the precise PSF. Our experiments on standard planes and actual objects show that the proposed algorithm can accurately describe the defocus process. The accuracy of our algorithm is further proved by evaluating the difference among the actual focused images, the focused image generated by our algorithm, the focused image generated by others. The results show that the loss of our algorithm is 40% less than others on average.

cs.CV↗

arXiv2022

Multi-task Learning for Monocular Depth and Defocus Estimations with Real Images

Monocular depth estimation and defocus estimation are two fundamental tasks in computer vision. Most existing methods treat depth estimation and defocus estimation as two separate tasks, ignoring the strong connection between them. In this work, we propose a multi-task learning network consisting of an encoder with two decoders to estimate the depth and defocus map from a single focused image. Through the multi-task network, the depth estimation facilitates the defocus estimation to get better results in the weak texture region and the defocus estimation facilitates the depth estimation by the strong physical connection between the two maps. We set up a dataset (named ALL-in-3D dataset) which is the first all-real image dataset consisting of 100K sets of all-in-focus images, focused images with focus depth, depth maps, and defocus maps. It enables the network to learn features and solid physical connections between the depth and real defocus images. Experiments demonstrate that the network learns more solid features from the real focused images than the synthetic focused images. Benefiting from this multi-task structure where different tasks facilitate each other, our depth and defocus estimations achieve significantly better performance than other state-of-art algorithms. The code and dataset will be publicly available at https://github.com/cubhe/MDDNet.

cs.CV↗