Image super-resolution with an enhanced group convolutional neural network
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TLDR
In this article , an enhanced super-resolution group CNN (ESRGCNN) was proposed by fully fusing deep and wide channel features to extract more accurate low-frequency information in terms of correlations of different channels in single image superresolution (SISR).About:
This article is published in Neural Networks.The article was published on 2022-09-01 and is currently open access. It has received 20 citations till now. The article focuses on the topics: Computer science & Convolutional neural network.read more
Citations
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Journal ArticleDOI
Interaction of Secure Cloud Network and Crowd Computing for Smart City Data Obfuscation
Manikandan Thirumalaisamy,Shajahan Basheer,S. Selvarajan,Sara Althubiti,Fayadh Alenezi,Gautam Srivastava,Chun-Wei Lin +6 more
TL;DR: The Averaged One-Dependence Estimators (AODE) classifier and the SELECT Applicable Only to Parallel Server (SELECT-APSL ASA) method are proposed to separate the data related to the place from smart city data based on the hybrid data obfuscation technique.
Journal ArticleDOI
GCS-YOLOV4-Tiny: A lightweight group convolution network for multi-stage fruit detection.
Mei-Ling Huang,Yi-Shan Wu +1 more
TL;DR: Wang et al. as discussed by the authors proposed a GCS-YOLOV4-Tiny model by adding squeeze and excitation (SE) and the spatial pyramid pooling (SPP) modules to improve the accuracy of the model and finally achieved faster detection speed.
Journal ArticleDOI
Semantic Segmentation of Side-Scan Sonar Images with Few Samples
TL;DR: A semantic segmentation model of side-scan sonar images based on a convolutional neural network is designed and tested, which is used to realize the semantic segmentated images with few training samples and its performance is improved, and the number of parameters is relatively small,which is easy to transplant to AUV.
Journal ArticleDOI
Baseline-independent stress classification based on facial StO2
Journal ArticleDOI
Iterative Dual CNNs for Image Deblurring
TL;DR: The proposed method has an excellent effect of deblurring on a real blurred image dataset and is suitable for various real application scenes and a multiscale iterative strategy that effectively improves the robustness and precision of the model.
References
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Journal ArticleDOI
Image Super-Resolution Using Deep Convolutional Networks
TL;DR: Zhang et al. as discussed by the authors proposed a deep learning method for single image super-resolution (SR), which directly learns an end-to-end mapping between the low/high-resolution images.
Journal ArticleDOI
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
TL;DR: Zhang et al. as mentioned in this paper proposed a feed-forward denoising convolutional neural networks (DnCNNs) to handle Gaussian denobling with unknown noise level.
Proceedings ArticleDOI
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
TL;DR: In this article, a very deep convolutional network inspired by VGG-net was used for image superresolution, which achieved state-of-the-art performance in accuracy.
Journal ArticleDOI
FSIM: A Feature Similarity Index for Image Quality Assessment
TL;DR: A novel feature similarity (FSIM) index for full reference IQA is proposed based on the fact that human visual system (HVS) understands an image mainly according to its low-level features.
Proceedings ArticleDOI
Enhanced Deep Residual Networks for Single Image Super-Resolution
TL;DR: This paper develops an enhanced deep super-resolution network (EDSR) with performance exceeding those of current state-of-the-art SR methods, and proposes a new multi-scale deepsuper-resolution system (MDSR) and training method, which can reconstruct high-resolution images of different upscaling factors in a single model.
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