Search · four archives
Search · four archives
21 papers · ranked by Valyu relevance
Rui Li, Artsemi Yushkevich, Xiaofeng Chu, Mikhail Kudryashev + 1 more
Computational image enhancement for microscopy facilitates cutting-edge biological discovery. While promising, the commonly used deep learning methods are computationally expensive owing to the use of general-purpose architectures, which are inefficient for microscopy data. Here, we propose a sparsity-efficient neural…
Jakub Czuchnowski, Chuan Li, Hongli Ni, Brandon Weissbourd + 1 more
Deconvolution is the most widely used aberration correction technique in microscopy, however most techniques assume that the aberrations are the same for each point in the image, which is rarely true. Methods for tracking spatially varying aberrations require burdensome calibration or computation, or require symmetries…
Ziwei Wang, Wanyu Gu, Shaolei Xu, Yupei Miao + 5 more
Computational fluorescence microscopy constantly breaks through imaging performance through advanced optical modulation technologies; however, conventional theoretical modeling and experimental measurement approaches are challenging to meet the demand for accurate system characterization of diverse modulations. To this…
Haohong Gan, Shiyi Peng, Hailian Hu, Xuan You + 4 more
The resolving power of optical microscopy is fundamentally constrained by the diffraction of light, limiting our ability to visualize subcellular structures. Computational methods, particularly deconvolution, can restore blurred images but critically depend on an accurate point spread function (PSF), whose estimation…
Yiming Liu, Sjoerd Stallinga
The gradient-consensus Richardson-Lucy (GC-RL) deconvolution algorithm is a novel approach to contrast restoration in high-resolution microscopy imaging without excessive noise amplification. We evaluate this method in this work, focusing on the impact of the noise level of the input image, of imperfections and…
Syed Mumtahin Mahmud, Mahdi Mohd Hossain Noki, Prothito Shovon Majumder, Abdul Mohaimen Al Radi + 2 more
Image deblurring is vital in computer vision, aiming to recover sharp images from blurry ones caused by motion or camera shake. While deep learning approaches such as CNNs and Vision Transformers (ViTs) have advanced this field, they often struggle with complex or high-resolution blur and computational demands. We…
Diptodip Deb, Gert-Jan Both, Eric Bezzam, Amit Kohli + 26 more
Modern microscopy methods incorporate computational modeling as an integral part of the imaging process, either to solve inverse problems or optimize the optical system design itself. These methods often depend on differentiable optics simulations, yet no standardized framework exists—forcing computational optics…
Diptodip Deb, Gert-Jan Both, Eric Bezzam, Amit Kohli + 26 more
Modem microscopy methods incorporate computational modeling as an integral part of the imaging process, either to solve inverse problems or optimize the optical system design itself. These methods often depend on differentiable optics simulations, yet no standardized framework exists-forcing computational optics…
Linh Hoang, Zhongqiang Li, Dominique Meyer, Xiankun Lu + 1 more
Studying biological processes across multi-millimeter scales requires imaging systems that combine high spatial resolution with a large field of view (FOV). However, optical aberrations degrade image quality, particularly in large-FOV systems where distortions gradually worsen toward the periphery. Existing methods for…
Guillermina Senn, Håkon Tjelmeland, Nathan Glatt-Holtz, Matt Walker + 1 more
Blind image deconvolution refers to the problem of simultaneously estimating the blur kernel and the true image from a set of observations when both the blur kernel and the true image are unknown. Sometimes, additional image and/or blur information is available and the term semi-blind deconvolution (SBD) is used. We…
James N. Caron
Image Phase Alignment Super-sampling (ImPASS) is a computational method for combining displaced low-resolution images into a single high-resolution image. The general steps include measuring the relative displacements, up-sampling, aligning and combining the images, followed by a blind deconvolution. Previous ImPASS…
Xiangyu Qian, Jing Liu, Yunqing Tang, Luru Dai + 1 more
Fluorescence microscopy images are degraded by noise and diffraction-induced blur, which compromise structural fidelity and limit quantitative analysis. Supervised deep learning methods achieve impressive restoration performance but require large-scale paired datasets that are difficult to obtain in practice. To…
Yaheng Wang, Junyong Fang, Xiaohong Zhang, Xiao Wang + 3 more
A periodic rotational-scanning panoramic imaging system (PRS imaging system) can acquire large-scale, continuous, and high-resolution panoramic images through rotational scanning. However, non-ideal camera motion during exposure introduces spatially varying motion blur, which degrades image quality and affects…
Nimrod Kruger, Nicholas Owen Ralph, Gregory Cohen, Paul Hurley
Event Vision Sensors, or neuromorphic cameras, report sparse, and asynchronous image change-related data and enable microsecond-scale sensing and high dynamic range, but challenge physics-based sensor design approaches. In response to log-intensity threshold-crossing instances, this event representation does not…
Arun D. Kulkarni
Shift-variant image degradation is frequently encountered in practical imaging systems where the point spread function (PSF) varies across the image field due to motion, optical aberrations, atmospheric turbulence, or sensor-related effects. Unlike shift-invariant, shift-variant degradation presents significant…
Ruijie Cao, Tong Jin, Fengyuan Xin, Yiwei Hou + 16 more
Three-dimensional (3D) imaging represents the development of next generation of fluorescence microscopy. However, routine axial down-sampling makes isotropic resolution unrealistic. Here, we propose DeepUI, a physical zero-shot framework designed to achieve isotropic 3D fluorescence images from a low axial sampling…
ShaoSen Chueh, Charlotte de Ceuninck van Capelle, Leo Luo, Takashi Ishikawa + 9 more
Soft X-ray tomography (SXT) is an emerging modality for whole-cell 3D imaging in near-native states. However, the effective spatial resolution is limited by optical artifacts characterized by the point spread function (PSF). To achieve optimal resolution via PSF inversion, we propose a measurement-supervised deep…
Arwa Dabbech, Yves Wiaux
Modern image formation algorithms in radio interferometry rely on repeated applications of the operator Φ modelling the measurement process and its adjoint {Phi^\dagger} to enforce consistency with the acquired data, specifically via their composite mapping {Phi^\daggerΦ} encoding the array's point spread function…
Authors not listed
A multi-fidelity Monte Carlo framework for molecular dynamics simulations of the diffusion coefficient of liquid water is presented. The model hierarchy is constructed based on the size of the simulation box, taking advantage of the well-known size effects that simulations of the diffusion coefficient suffer from.…
Authors not listed
The rapid growth of worldwide computing power has transformed in silico chemistry into a discipline that is integrated into the daily work of many chemists. Nowadays, researchers find it increasingly straightforward to predict a wide range of molecular properties and chemi- cal processes at reasonable computational…
Authors not listed
The complete active space self-consistent field (CASSCF) method is essential for describing complex photochemical processes, but its application in ab initio molecular dynamics is often limited by the computational cost associated with four-center two-electron repulsion integrals (ERIs). We present the first…