Results 31 to 40 of about 47,616 (177)
Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution
, 2016 In this work, we consider the image super-resolution (SR) problem. The main challenge of image SR is to recover high-frequency details of a low-resolution (LR) image that are important for human perception.
Feng, Jiashi +6 more
core +1 more source
On the Power of Adaptivity in Sparse Recovery [PDF]
, 2011 The goal of (stable) sparse recovery is to recover a $k$-sparse approximation $x*$ of a vector $x$ from linear measurements of $x$. Specifically, the goal is to recover $x*$ such that ||x-x*||_p
Indyk, Piotr +2 more
core +4 more sources
A Review of Radar Signal Processing Based on Sparse Recovery
Leida xuebaoWith the growing demand for radar target detection, Sparse Recovery (SR) technology based on the Compressive Sensing (CS) model has been widely used in radar signal processing.
Yinghui QUAN +6 more
doaj +1 more source
Linear Regression Based Clutter Reconstruction for STAP
IEEE Access, 2018 Space-time adaptive processing (STAP) is supposed to be a crucial technique for improving target detection performance in a strong clutter background for airborne phased array radar systems.
Wei Zhang, Zishu He, Huiyong Li
doaj +1 more source
Advanced Functional Materials, EarlyView.A self‐gelling PG@PAC (POD/Gel‐CDH@PA/CHX) powder is developed for infected burn care in austere settings. Upon contact with wound exudate, it instantly forms an adhesive hydrogel, providing simultaneous hemostasis, broad‐spectrum antibacterial activity, reactive oxygen species scavenging, and immunomodulation. In a murine model of S.
Liping Zhang +14 more
wiley +1 more source
ADMM-Based Low-Complexity Off-Grid Space-Time Adaptive Processing Methods
IEEE Access, 2020 In this paper, we consider the problems of off-grid effects elimination and fast implementations for sparse recovery based space-time adaptive processing (SR-STAP) methods.
Zhongyue Li, Tong Wang
doaj +1 more source
C-HiLasso: A Collaborative Hierarchical Sparse Modeling Framework
, 2010 Sparse modeling is a powerful framework for data analysis and processing. Traditionally, encoding in this framework is performed by solving an L1-regularized linear regression problem, commonly referred to as Lasso or Basis Pursuit.
Eldar, Yonina +3 more
core +4 more sources
Unveiling Multidimensional Physicochemical Design Principles for Tissue Processing Hydrogels
Advanced Functional Materials, EarlyView.This study establishes a materials‐based design framework for polymer hydrogels in tissue clearing, linking physicochemical properties to performance in tissue processing, labeling, and imaging. By analyzing rheology, swelling, porosity, antibody diffusion, mechanical performance, and thermochemical stability across platforms, this work provides a ...
Sangjae Kim +8 more
wiley +1 more source
Fast and Gridless Sparse Recovery STAP Method Based on Nonconvex Relaxation of Atomic Norm
Leida xuebaoSparse Recovery-based Space-Time Adaptive Processing (SR-STAP) methods offer significant advantages in nonhomogeneous clutter environments owing to their minimal requirement for training samples.
Linfeng CUI +3 more
doaj +1 more source
Structured Sparsity Models for Multiparty Speech Recovery from Reverberant Recordings [PDF]
, 2012 We tackle the multi-party speech recovery problem through modeling the acoustic of the reverberant chambers. Our approach exploits structured sparsity models to perform room modeling and speech recovery.
Asaei, Afsaneh +3 more
core +2 more sources