Results 81 to 90 of about 9,140,641 (221)
Photon‐Sphere Modes in Curved Optical Microcavities: A Black‐Hole Analogue Laser
An optical analogue of a Schwarzschild black hole is realized using curved microcavities that preserve light‐like geodesics. A new family of laser modes confined around the photon sphere is identified alongside conventional whispering‐gallery modes. Analytical theory, numerical simulations, and experiments reveal curvature‐induced confinement, enabling
Chenni Xu +9 more
wiley +1 more source
Variational semi-blind sparse deconvolution with orthogonal kernel bases and its application to MRFM [PDF]
We present a variational Bayesian method of joint image reconstruction and point spread function (PSF) estimation when the PSF of the imaging device is only partially known.
Se Un Parka +5 more
core +1 more source
Deep Learning Integration in Optical Microscopy: Advancements and Applications
It explores the integration of DL into optical microscopy, focusing on key applications including image classification, segmentation, and computational reconstruction. ABSTRACT Optical microscopy is a cornerstone imaging technique in biomedical research, enabling visualization of subcellular structures beyond the resolution limit of the human eye ...
Pottumarthy Venkata Lahari +5 more
wiley +1 more source
Data-driven gradient priors integrated into blind image deblurring
Blind image deblurring is a severely ill-posed task. Most existing methods focus on deep learning to learn massive data features while ignoring the vital significance of classic image structure priors.
Li, Chongyi, Guo, Jichang, Qing, Qi
core +1 more source
ABSTRACT Purpose To achieve high resolution (≤ 1 mm isotropic) whole‐brain perfusion imaging at 7 T with next generation ASL pulse sequence, reconstruction algorithm, and MRI hardware. Methods We capitalized on three major innovations: (1) FLASH‐based pseudo‐Continuous ASL (pCASL) sequence with rotated golden‐angle stack‐of‐spirals (rGA‐SoS) sampling; (
Chenyang Zhao +8 more
wiley +1 more source
Image Deblurring with a Class-Specific Prior
A fundamental problem in image deblurring is to recover reliably distinct spatial frequencies that have been suppressed by the blur kernel. To tackle this issue, existing image deblurring techniques often rely on generic image priors such as the sparsity
Huynh, Cong +2 more
core +1 more source
Text image deblurring using kernel sparsity prior [PDF]
Previous methods on text image motion deblurring seldom consider the sparse characteristics of the blur kernel. This paper proposes a new text image motion deblurring method by exploiting the sparse properties of both text image itself and kernel.
Shao, Ling +4 more
core +1 more source
DEEP‐DISORDER: Motion Correction in 3D MRI via Segment Reconstruction and Registration
This work presents a retrospective motion correction framework for 3D MRI. A motion‐corrupted acquisition is split into tiny k‐space segments, and for each a neural network reconstructs a rough anatomical image. These reconstructions are aligned using groupwise registration, yielding one set of estimated motion parameters per segment.
Laurens Beljaards +7 more
wiley +1 more source
Fixing a blurred photograph: blind image deblurring
This project presents a deep learning-based approach to blind image deblurring using a convolutional neural network. The trained model can produce a deblurred output using only the blurred image as input and exhibits improved image quality, as ...
Teo, Hong Wei
core
Deep Regressor Networks for Blind Image Deblurring
Image restoration concerns mainly smoothing noise and deblurring images that were corrupted either during acquisition or transmission. Since traditional deconvolution filters are highly dependent on specific kernels or prior knowledge to guide the ...
Rafael G. Pires +7 more
core +1 more source

