Single image super-resolution via multi-scale residual channel attention network
Sign Up to like & getrecommendations! Published in 2019 at "Neurocomputing"
DOI: 10.1016/j.neucom.2019.05.066
Abstract: Abstract Recently, various convolutional neural networks (CNNs) based single image super-resolution (SR) methods have been vigorously explored, and a lot of impressive results have emerged. However, more or less unfortunately, most of the methods mainly… read more here.
Keywords: scale residual; channel attention; multi scale; network ... See more keywords
Three-dimensional residual channel attention networks denoise and sharpen fluorescence microscopy image volumes.
Sign Up to like & getrecommendations! Published in 2021 at "Nature methods"
DOI: 10.1038/s41592-021-01155-x
Abstract: We demonstrate residual channel attention networks (RCAN) for the restoration and enhancement of volumetric time-lapse (four-dimensional) fluorescence microscopy data. First we modify RCAN to handle image volumes, showing that our network enables denoising competitive with… read more here.
Keywords: resolution; image; microscopy; attention networks ... See more keywords
Neural network pruning based on channel attention mechanism
Sign Up to like & getrecommendations! Published in 2022 at "Connection Science"
DOI: 10.1080/09540091.2022.2111405
Abstract: Network pruning facilitates the deployment of convolutional neural networks in resource-limited environments by reducing redundant parameters. However, most of the existing methods ignore the differences in the contributions of the output feature maps. In response… read more here.
Keywords: network pruning; attention mechanism; channel attention;
A deep residual inception network with channel attention modules for multi-label cardiac abnormality detection from reduced-lead ECG
Sign Up to like & getrecommendations! Published in 2022 at "Physiological Measurement"
DOI: 10.1088/1361-6579/ac6f40
Abstract: Objective. Most arrhythmias due to cardiovascular diseases alter the heart’s electrical activity, resulting in morphological alterations in electrocardiogram (ECG) recordings. ECG acquisition is a low-cost, non-invasive process and is commonly used for continuous monitoring as… read more here.
Keywords: inception; network; lead ecg; channel attention ... See more keywords
FCA-Net: Adversarial Learning for Skin Lesion Segmentation Based on Multi-Scale Features and Factorized Channel Attention
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2940418
Abstract: Skin lesion segmentation in dermoscopic images is still a challenge due to the low contrast and fuzzy boundaries of lesions. Moreover, lesions have high similarity with the healthy regions in terms of appearance. In this… read more here.
Keywords: lesion segmentation; lesion; channel attention; skin lesion ... See more keywords
Unpaired Stain Style Transfer Using Invertible Neural Networks Based on Channel Attention and Long-Range Residual
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Access"
DOI: 10.1109/access.2021.3051188
Abstract: Hematoxylin and eosin (H&E) stained colors is a critical step in the digitized pathological diagnosis of cancer. However, differences in section preparations, staining protocols and scanner specifications may result in the variations of stain colors… read more here.
Keywords: channel attention; stain style; transfer; attention long ... See more keywords
MPCNet: Improved MeshSegNet Based on Position Encoding and Channel Attention
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Access"
DOI: 10.1109/access.2023.3254206
Abstract: In the process of orthodontic treatment, it is a very important step to accurately segment each tooth and jaw model with computer assistance. The use of deep learning technology methods for tooth segmentation can not… read more here.
Keywords: mpcnet; segmentation; tooth segmentation; channel attention ... See more keywords
Channel-Attention-Based DenseNet Network for Remote Sensing Image Scene Classification
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"
DOI: 10.1109/jstars.2020.3009352
Abstract: Remote sensing image scene classification has been widely applied and has attracted increasing attention. Recently, convolutional neural networks (CNNs) have achieved remarkable results in scene classification. However, scene images have complex semantic relationships between multiscale… read more here.
Keywords: attention; scene classification; network; channel attention ... See more keywords
Enhanced Channel Attention Network With Cross-Layer Feature Fusion for Spectral Reconstruction in the Presence of Gaussian Noise
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"
DOI: 10.1109/jstars.2022.3218820
Abstract: Spectral reconstruction from RGB images has made significant progress. Previous works usually utilized the noise-free RGB images as input to reconstruct the corresponding hyperspectral images (HSIs). However, due to instrumental limitation or atmospheric interference, it… read more here.
Keywords: gaussian noise; reconstruction; channel attention; spectral reconstruction ... See more keywords
Edge Enhanced Channel Attention-Based Graph Convolution Network for Scene Classification of Complex Landscapes
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"
DOI: 10.1109/jstars.2023.3265677
Abstract: Monitoring the land covers in complex landscapes is of great significance for the sustainable development of mine geo-environments. As most existing remote sensing scene datasets are composed of RGB images, there is a lack of… read more here.
Keywords: classification; edge enhanced; channel attention; enhanced channel ... See more keywords
Multilevel Progressive Network With Nonlocal Channel Attention for Hyperspectral Image Super-Resolution
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2022.3221550
Abstract: Deep convolutional neural networks (CNNs) have made great progress in the super-resolution (SR) of hyperspectral images (HSIs). However, most methods utilize convolution to explore local features, and global features are ignored. It is expected that… read more here.
Keywords: network; super resolution; multilevel progressive; channel attention ... See more keywords