Results 91 to 100 of about 6,876,175 (322)
An Improved Combination of Image Denoisers Using Spatial Local Fusion Strategy
IEEE Access, 2020 Image denoising is a well-researched problem in the image processing field. Numerous image denoising algorithms have been proposed in the past. Although researchers have continually focused on improving the denoising algorithm performance regarding ...
Yiwen Liu, Shaoping Xu, Zhenyu Lin
doaj +1 more source
Advanced Engineering Materials, EarlyView.New features on yttria‐stabilized zirconia after exposure at 1500°C: Newly discovered pyramidal structures on an old material. After exposure at 1550°C on the cross section of YSZ new features, namely pyramidal structures are discovered. These structures grow with time, increase in numbers, appear as singularities, are often arranged in strings, and ...
Doris Sebold +2 more
wiley +1 more source
Image denoising with multi-layer perceptrons, part 1: comparison with existing algorithms and with bounds [PDF]
, 2012 Image denoising can be described as the problem of mapping from a noisy image to a noise-free image. The best currently available denoising methods approximate this mapping with cleverly engineered algorithms.
Burger, Harold Christopher +2 more
core
Deep Graph Laplacian Regularization for Robust Denoising of Real Images
, 2019 Recent developments in deep learning have revolutionized the paradigm of image restoration. However, its applications on real image denoising are still limited, due to its sensitivity to training data and the complex nature of real image noise.
Cheung, Gene +3 more
core +1 more source
Multimodal Data‐Driven Microstructure Characterization
Advanced Engineering Materials, EarlyView.A self‐consistent autonomous workflow for EBSP‐based microstructure segmentation by integrating PCA, GMM clustering, and cNMF with information‐theoretic parameter selection, requiring no user input. An optimal ROI size related to characteristic grain size is identified.
Qi Zhang +4 more
wiley +1 more source
Deep Neural Network for Image Super Resolution Driven by Prior Denoising
Nantong Daxue xuebao. Ziran kexue ban, 2021 In order to improve image super resolution, a double layer convolution neural network in image denoising is embedded in image restoration tasks. The image super resolution method driven by prior denoising with deep neural network is proposed.
CHENG Fanqiang;ZHU Yonggui;, ZHU Yonggui
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Mapping Nanoscale Buckling in Atomically Thin Cr2Ge2Te6
Advanced Functional Materials, EarlyView.Atomic‐resolution STEM is used to resolve nanoscale buckling in monolayer Cr2Ge2Te₆. A noise‐robust image analysis reconstructs three‐dimensional lattice distortions from single plan‐view images, revealing pronounced defect‐driven nm‐scale out‐of‐plane buckling.
Amy Carl +20 more
wiley +1 more source
Multi-View Image Denoising Using Convolutional Neural Network
Sensors, 2019 In this paper, we propose a novel multi-view image denoising algorithm based on convolutional neural network (MVCNN). Multi-view images are arranged into 3D focus image stacks (3DFIS) according to different disparities.
Shiwei Zhou, Yu-Hen Hu, Hongrui Jiang
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, 2006 The paper is concerned with the problem of image denoising for the case of grey-scale images. Such images consist of a finite number of regions with smooth boundaries and the image value is assumed piecewise constant within each region. New method of image denoising is proposed which is adaptive (assumption free) to the number of regions and smoothness
Polzehl, Jörg, Spokoiny, Vladimir
openaire +1 more source
Wavelet Bayesian Network Image Denoising [PDF]
IEEE Transactions on Image Processing, 2013 From the perspective of the Bayesian approach, the denoising problem is essentially a prior probability modeling and estimation task. In this paper, we propose an approach that exploits a hidden Bayesian network, constructed from wavelet coefficients, to model the prior probability of the original image.
Jinn, Ho, Wen-Liang, Hwang
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