Results 201 to 210 of about 259,715 (355)

Iterative projective wavelet methods for denoising [PDF]

, 2003
Alyson K. Fletcher, Vivek K Goyal, Kannan Ramchandran   +2 more
openalex   +1 more source

Germanium‐Based Long‐Conjugation Xanthene Fluorophores for Bioimaging in Optimal NIR‐II Sub‐Window

Aggregate, EarlyView.
An NIR‐II dye (EGe5) integrates germanium‐enhanced vibrational suppression and extended conjugation, achieving unprecedented brightness in the optimal NIR‐IIx sub‐window. This design enables high‐contrast visualization of intestinal vasculature and renal labeling, overcoming traditional signal limitations. The tailored spectral properties highlight its
Jin Li, Qiming Xia, Jiayi Li, Xiaoming Yu, Zhe Feng, Yuhuang Zhang, Tianxiang Wu, Zhongmin Xu, Hui Lin, Jun Qian   +9 more
wiley   +1 more source

Denoising Functional MR Images: A Comparison of Wavelet Denoising and Gaussian Smoothing [PDF]

, 2004
Alle Meije Wink, J.B.J.M. Roerdink
openalex   +1 more source

Denoising Designs-inherited Search Framework for Image Denoising

How to benefit from plenty of existing denoising designs? Few methods via Neural Architecture Search (NAS) intend to answer this question. However, these NAS-based denoising methods explore limited search space and are hard to extend in terms of search space due to high computational burden.
Zhang, Zheyu, Zhang, Yueyi, sun, Xiaoyan
openaire   +2 more sources

AI‐Enhanced Surface‐Enhanced Raman Scattering for Accurate and Sensitive Biomedical Sensing

Advanced Intelligent Discovery, EarlyView.
AI‐SERS advances spectral interpretation with greater precision and speed, enhancing molecular detection, biomedical analysis, and imaging. This review explores its essential contributions to biofluid analysis, disease identification, therapeutic agent evaluation, and high‐resolution biomedical imaging, aiding diagnostic decision‐making.
Seungki Lee, Rowoon Park, Ho Sang Jung
wiley   +1 more source

Wavelet-based MAP image denoising using provably better class of stochastic i.i.d. image models

, 2002
Ivan Prudyus, Slava Voloshynovskiy, Andriy Synyavskyy   +2 more
openalex   +2 more sources

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Smoothness Estimates for Soft-Threshold Denoising via Translation-Invariant Wavelet Transforms

, 2002
Kathrin Berkner, Raymond O. Wells
openalex   +1 more source

Real-time self-supervised denoising for high-speed fluorescence neural imaging. [PDF]

Nat Commun
Wang Y, Gu Y, Wang J, Xuan A, Kong C, Fang WQ, Li D, Zhu D, Ding F, Dong B.   +9 more
europepmc   +1 more source

A Solution for Exosome‐Based Analysis: Surface‐Enhanced Raman Spectroscopy and Artificial Intelligence

Advanced Intelligent Discovery, EarlyView.
Exosomes are emerging as powerful biomarkers for disease diagnosis and monitoring. This review highlights the integration of surface‐enhanced Raman spectroscopy with artificial intelligence to enhance molecular fingerprinting of exosomes. Machine learning and deep learning techniques improve spectral interpretation, enabling accurate classification of ...
Munevver Akdeniz, Zakarya Al‐Shaebi, Omer Aydin   +2 more
wiley   +1 more source