20 papers · ranked by Valyu relevance
Jean-Eric Campagne
Aims: This study investigates whether a U-Net architecture can perform standalone end-to-end blind deconvolution of astronomical images without any prior knowledge of the Point Spread Function (PSF) or noise characteristics. Our goal is to evaluate its performance against the number of training images, classical…
Mirza Karamehmedovic, Pierre Maréchal, Martin Sæbye Carøe, Lara Baalbaki
We extend the classical deconvolution framework in R n to the case with a pseudodifferential-like solution operator with a symbol depending on both the base and cotangent variable. Our framework enables deconvolution with spatially varying resolution while maintaining a set global stability, and it additionally allows…
Bin Fu, Caroline L. Jones, Daniel Heraghty, Shengbo Yang + 8 more
Imaging flow cytometry using Fourier light-field microscopy enables high-throughput three-dimensional cellular imaging, capable of capturing thousands of events per second. However, volumetric reconstruction speed remains orders of magnitude slower than the acquisition speed. The current state of art uses…
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…
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…
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…
Peter Kirchweger, Lev Melnikovsky, Shahar Seifer, Michael Elbaum
Cryo-electron tomography is an expanding technology for the study of macromolecules, viruses, and cells. It is often applied to specimens that are too large or heterogeneous for methods based on 2D image averaging such as single particle analysis, e.g., intracellular membranes or organelles. Current practice records a…
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…
Wenjun Shen, Yunfei Hu, Yuanfang Lei, Hau-San Wong + 3 more
Accurate deconvolution of bulk and spatial transcriptomes is essential for studying tissue architecture and disease, yet remains challenged by unmodeled differences in cellular RNA content and cross-source heterogeneity. We introduce CSsingle, a unified deconvolution framework that explicitly corrects for…
Hao Chen, Scott S. Howard
Richardson--Lucy (RL) deconvolution improves fluorescence microscopy images by recovering details lost to diffraction. It estimates the original fluorescence signal that most likely produced the measured photon counts under a Poisson imaging model. Although RL incorporates a physical model of fluorescence image…
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…
Timur E. Gureyev, David M. Paganin, Ashkan Pakzad, Harry M. Quiney
Paganin's method for image reconstruction in propagation-based phase-contrast X-ray imaging and tomography has enjoyed broad acceptance in recent years, with over one thousand publications citing its use. The present paper discusses approaches to optimisation of the method with respect to simple image quality metrics…
Lukas Grunewald, Petra Meszaros, Sebastian Westenhoff
Time-resolved serial crystallography (TR-SX) has emerged as a powerful method for capturing ultrafast structural dynamics in proteins. TR-SX continues to produce remarkable studies, revealing previously unobserved transient states and providing deeper insights into processes such as drug targeting, DNA repair, and…
Yifan Ma, Tianfeng Zhou, Lanxin Zhu, Chengqiang Yi + 3 more
The application of emerging Foundation models in current microscopy has remained limited due to task-specific model designs, strong modality dependence and insufficient robustness under complex degradations. Here, we introduce MAGNET (Microscopic All-in-one General fouNdation model for imagE resToration), the first…
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…
Authors not listed
The combinatorial explosion inherent to multi-component systems limits their experimental exploration and ultimately chemical discovery. Here, we introduce a statistics-based group-testing strategy, which we couple with luminescence quenching assays to efficiently identify cooperative molecular interactions. Utilizing…
Authors not listed
Comprehensive characterisation of monoclonal antibody (mAb) charge heterogeneity is essential for ensuring product quality, maintaining batch consistency, and supporting biosimilar development. Charge variant analysis (CVA) is widely used to separate acidic and basic proteoforms from the main species. However…
Wonsang Hwang, Iván Coto Hernández, Conor L. Evans
Quantitative fluorescence imaging techniques such as fluorescence lifetime imaging microscopy and hyperspectral imaging infer molecular contrast from photons distributed across spatial pixels and temporal or spectral channels. In the fewphoton regime, however, conventional pixel-wise analysis discards the spatial…
Authors not listed
Dion-Jacobson (DJ) quasi-two-dimensional (2D) halide perovskites (HPs) have emerged as a robust platform for optoelectronic applications, yet their crystallization pathways, encompassing nucleation, growth and phase transformation, under non-equilibrium synthesis conditions, remains insufficiently understood. Herein…
Authors not listed
We introduce an open-source program that converts molecular dynamics trajectories into disconnectivity graphs, providing a concise and interpretable visualisation of the energy landscape that has been traversed. Our approach applies Savitzky–Golay smoothing to per-frame thermodynamic traces (potential energy in NVE/NVT…