High-Frequency Attention Residual GAN Network for Blind Motion Deblurring
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3194524
Abstract: The moving image deblurring method based on deep learning has achieved good results. However, some methods are not effective in restoring image texture detail information. Therefore, this paper proposes a High-Frequency Attention Residual Module (HFAR),… read more here.
Keywords: high frequency; frequency; attention residual; module ... See more keywords
Spatial–Spectral Split Attention Residual Network for Hyperspectral Image Classification
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.2022.3225928
Abstract: In the past few years, many convolutional neural networks (CNNs) have been applied to hyperspectral image (HSI) classification. However, many of them have the following drawbacks: they do not fully consider the abundant band spectral… read more here.
Keywords: classification; split attention; attention residual; attention ... See more keywords
DARN: Distance Attention Residual Network for Lightweight Remote-Sensing Image Superresolution
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.2022.3227509
Abstract: The application of single-image superresolution (SISR) in remote sensing is of great significance. Although the state-of-the-art convolution neural network (CNN)-based SISR methods have achieved excellent results, the large model and slow speed make it difficult… read more here.
Keywords: distance attention; darn; remote sensing; attention residual ... See more keywords
Progressive Dual-Attention Residual Network for Salient Object Detection
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Circuits and Systems for Video Technology"
DOI: 10.1109/tcsvt.2022.3164093
Abstract: Due to the rapid development of deep learning, the performance of salient object detection has been constantly refreshed. Nevertheless, it is still challenging for existing methods to distinguish the location of salient objects and retain… read more here.
Keywords: network; salient object; dual attention; attention residual ... See more keywords
Embedded Control Gate Fusion and Attention Residual Learning for RGB–Thermal Urban Scene Parsing
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Intelligent Transportation Systems"
DOI: 10.1109/tits.2023.3242651
Abstract: The semantic segmentation of road scenes is an important task in autonomous driving. Deep learning has enabled the development of a variety of semantic segmentation networks using RGB and depth data. However, poor lighting conditions… read more here.
Keywords: embedded control; residual learning; learning; segmentation ... See more keywords
Crowd counting via an inverse attention residual network
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Electronic Imaging"
DOI: 10.1117/1.jei.29.3.033010
Abstract: Abstract. Deep learning has enabled substantial progress in crowd counting, but existing methods still face difficulties due to significant scale variations, severe occlusion, and overcrowding. To explicitly address these problems, we propose a crowd counting… read more here.
Keywords: attention residual; crowd counting; inverse attention; network ... See more keywords