Results 191 to 200 of about 43,627 (311)

SuperResNET: Model‐Free Single‐Molecule Network Analysis Software Achieves Molecular Resolution of Nup96

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
SuperResNET is a powerful integrated software that reconstructs network architecture and molecular distribution of subcellular structures from single molecule localization microscopy datasets. SuperResNET segments the nuclear pore complex and corners, extracts size, shape, and network features of all segmented nuclear pores and uses modularity analysis
Yahongyang Lydia Li, Ismail M. Khater, Christian Hallgrimson, Ben Cardoen, Timothy H. Wong, Ghassan Hamarneh, Ivan R. Nabi   +6 more
wiley   +1 more source

BDNet: A Real-Time Biomedical Image Denoising Network with Gradient Information Enhancement Loss. [PDF]

Biosensors (Basel)
Shi L, Feng X, Gong P, Song D, Zhang H, Liu L, Zhang Y, Li M.   +7 more
europepmc   +1 more source

Neuromorphic Denoising with Fully Analog Memristive In‐Memory Computing

Advanced Intelligent Systems, EarlyView.
This article borrows the concepts of episodic memory in human brains to experimentally implement a memristor‐based neuromorphic denoising process. A homogeneous memristor processing unit is experimentally demonstrated for both temporal storage and neural network computation, imitating the synapses in the human brain.
Daijing Shi, Teng Zhang, Yuqi Li, Yaoyu Tao, Bonan Yan, Yuchao Yang   +5 more
wiley   +1 more source

Noise-augmented deep denoising: A method to boost CT image denoising networks. [PDF]

Med Phys
Kristof G, Eulig E, Kachelrieß M.
europepmc   +1 more source

An Integrated and Robust Deep Learning Framework for Denoising and Analyzing Single‐Cell Spatial Transcriptomics

Advanced Intelligent Systems, EarlyView.
Single‐cell Spatial Transcriptomics Analysis and Denoising Engine is introduced as a unified deep learning framework that jointly performs denoising, clustering, and gene prioritization in spatial transcriptomics. By integrating linear and nonlinear representations within a dual‐channel architecture, it improves robustness and accuracy, uncovers ...
Yaxuan Cui, Yang Cui, Ruheng Wang, Xin Zeng, Zheyong Zhu, Ding Yi, Ruihua Guo, Tetsuya Sakurai, Kenta Nakai, Xiucai Ye, Leyi Wei, Yong Chen   +11 more
wiley   +1 more source

MDSC: Unsupervised Multipath Decomposition and Synergistic Correction for Efficient Low‐Light Image Enhancement with Detail‐Aware Smoothing

Advanced Intelligent Systems, EarlyView.
This work proposes MDSC, an unsupervised low‐light enhancement framework integrating three core innovations: detail‐aware smoothing, multipath decomposition, and synergistic correction. It suppresses noise, handles rapid illumination variations, and prevents reflectance‐contrast amplification inherent to Retinex separation.
Yong Cheng, De Yu, Yongqi Yuan, Mengjie Ye, Huan Wang, Qian Zhang, Ge Jin   +6 more
wiley   +1 more source

Block compressive sensing-based image denoising framework using optimized sensing matrix and split Bregman algorithm. [PDF]

Sci Rep
Thomas EN, Theeda P, Praveen T.
europepmc   +1 more source

"Denoising an image by denoising its curvature image."

SIAM Journal on Imaging Sciences 7.1 (2014): 187-211., 2017
Bertalmxedo, Marcelo, Stacey Levine
openaire  

Robust Representation Learning for Clean Feature Discovery in Incomplete Multi‐View Clustering

Advanced Intelligent Systems, EarlyView.
Robust feature discovery in incomplete multi‐view clustering is achieved by coupling RPCA‐based clean representation recovery with neural‐network‐assisted graph learning. The resulting RIMVC framework constructs cleaner and more discriminative graph‐structured representations from incomplete and noisy multi‐view data, improving clustering robustness ...
Ping Hu, Chenggang Lu, Rui Zhang, Haijun Yan, Yesong Xu   +4 more
wiley   +1 more source

Sonar image denoising based on clustering and Bayesian sparse coding. [PDF]

PLoS One
Xing C, Bao D, Huang T, Meng Y.
europepmc   +1 more source