Classification of breast cancer histopathological image with deep residual learning
Sign Up to like & getrecommendations! Published in 2021 at "International Journal of Imaging Systems and Technology"
DOI: 10.1002/ima.22548
Abstract: Breast cancer has high incidences and mortality rates in women worldwide. Malignancy could be detected manually by experienced pathologists based on Hematoxylin and Eosin (H&E) stained images. However, it is time‐consuming and experience‐dependent, making early… read more here.
Keywords: residual learning; classification; image; breast cancer ... See more keywords
Deep residual learning for image steganalysis
Sign Up to like & getrecommendations! Published in 2017 at "Multimedia Tools and Applications"
DOI: 10.1007/s11042-017-4440-4
Abstract: Image steganalysis is to discriminate innocent images and those suspected images with hidden messages. This task is very challenging for modern adaptive steganography, since modifications due to message hiding are extremely small. Recent studies show… read more here.
Keywords: image steganalysis; residual learning; deep residual;
Image interpolation using convolutional neural networks with deep recursive residual learning
Sign Up to like & getrecommendations! Published in 2019 at "Multimedia Tools and Applications"
DOI: 10.1007/s11042-019-7633-1
Abstract: Recent developments of image super-resolution often utilize the deep convolutional neural network (CNN) and residual learning to relate the observed low-resolution pixels and unknown high-resolution pixels. However, image interpolation assumes that the observed image was… read more here.
Keywords: resolution; image interpolation; image; residual learning ... See more keywords
Learning deep hierarchical and temporal recurrent neural networks with residual learning
Sign Up to like & getrecommendations! Published in 2020 at "International Journal of Machine Learning and Cybernetics"
DOI: 10.1007/s13042-020-01063-0
Abstract: Learning both hierarchical and temporal dependencies can be crucial for recurrent neural networks (RNNs) to deeply understand sequences. To this end, a unified RNN framework is required that can ease the learning of both the… read more here.
Keywords: learning deep; neural networks; hierarchical temporal; residual learning ... See more keywords
Video Super-Resolution via Residual Learning
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Access"
DOI: 10.1109/access.2018.2829908
Abstract: Convolutional neural networks have been widely applied in many low level vision tasks. In this paper, we propose a video super-resolution (SR) method named enhanced video SR network with residual blocks (EVSR). The proposed EVSR… read more here.
Keywords: video; video super; resolution via; super resolution ... See more keywords
Shakedrop Regularization for Deep Residual Learning
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2960566
Abstract: Overfitting is a crucial problem in deep neural networks, even in the latest network architectures. In this paper, to relieve the overfitting effect of ResNet and its improvements (i.e., Wide ResNet, PyramidNet, and ResNeXt), we… read more here.
Keywords: shakedrop regularization; regularization deep; residual learning; deep residual ... See more keywords
Progressive Residual Learning With Memory Upgrade for Ultrasound Image Blind Super-Resolution
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Journal of Biomedical and Health Informatics"
DOI: 10.1109/jbhi.2022.3142076
Abstract: For clinical medical diagnosis and treatment, image super-resolution (SR) technology will be helpful to improve the ultrasonic imaging quality so as to enhance the accuracy of disease diagnosis. However, due to the differences of sensing… read more here.
Keywords: module; residual learning; resolution; ultrasound image ... See more keywords
Deep Siamese Cross-Residual Learning for Robust Visual Tracking
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2020.3041052
Abstract: The sixth-generation (6G) wireless technology contributes to the establishment of the Internet of Things (IoT). Recently, the IoT has become popular because of its smart architectures and various applications. Among these applications, intelligent urban surveillance… read more here.
Keywords: visual tracking; cross residual; siamese cross; residual learning ... See more keywords
An Efficient Residual Learning Neural Network for Hyperspectral Image Superresolution
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"
DOI: 10.1109/jstars.2019.2901752
Abstract: Deep learning, especially a discriminative model for image reconstruction, has shown great potential for single image superresolution (SR) of hyperspectral images (HSI). For HSI SR task, it is crucial to predicting each pixel according to… read more here.
Keywords: neural network; image; residual learning; image superresolution ... See more keywords
Deep Residual Learning for Boosting the Accuracy of Hyperspectral Pansharpening
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2019.2945424
Abstract: Recently, deep learning (DL) has gained impressive achievements in the field of remote sensing image fusion. However, most of the previous DL-based fusion methods are originally designed for multispectral pansharpening, which cannot be readily employed… read more here.
Keywords: accuracy hyperspectral; learning boosting; deep residual; hyperspectral pansharpening ... See more keywords
A New Multiscale Residual Learning Network for HSI Inconsistent Noise Removal
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2020.3037104
Abstract: Hyperspectral image (HSI) often suffers from various noise disturbances which makes the interpretation difficult. To solve this problem, a lot of HSI denoising algorithms have been proposed and widely used. Although many convolution neural network… read more here.
Keywords: noise; inconsistent noise; new multiscale; residual learning ... See more keywords