Multi-scale dilated convolution of convolutional neural network for image denoising

2019 ◽  
Vol 78 (14) ◽  
pp. 19945-19960 ◽  
Author(s):  
Yanjie Wang ◽  
Guodong Wang ◽  
Chenglizhao Chen ◽  
Zhenkuan Pan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Denoising ◽  
Multi Scale ◽  
Dilated Convolution

Multi-scale dilated convolution of convolutional neural network for crowd counting

2019 ◽  
Vol 79 (1-2) ◽  
pp. 1057-1073 ◽  
Author(s):  
Yanjie Wang ◽  
Shiyu Hu ◽  
Guodong Wang ◽  
Chenglizhao Chen ◽  
Zhenkuan Pan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Crowd Counting ◽  
Multi Scale ◽  
Dilated Convolution

A novel multi-scale convolutional neural network for motor imagery classification

2021 ◽  
Vol 68 ◽  
pp. 102747
Author(s):  
Mouad Riyad ◽  
Mohammed Khalil ◽  
Abdellah Adib
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Motor Imagery ◽  
Multi Scale

scholarly journals A Multi-Scale Feature Extraction-Based Normalized Attention Neural Network for Image Denoising

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 319
Author(s):  
Yi Wang ◽  
Xiao Song ◽  
Guanghong Gong ◽  
Ni Li
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Image Denoising ◽  
Color Image ◽  
Rapid Development ◽  
Similarity Index ◽  
Structural Similarity ◽  
Convolutional Network ◽  
Scale Feature ◽  
Multi Scale

Due to the rapid development of deep learning and artificial intelligence techniques, denoising via neural networks has drawn great attention due to their flexibility and excellent performances. However, for most convolutional network denoising methods, the convolution kernel is only one layer deep, and features of distinct scales are neglected. Moreover, in the convolution operation, all channels are treated equally; the relationships of channels are not considered. In this paper, we propose a multi-scale feature extraction-based normalized attention neural network (MFENANN) for image denoising. In MFENANN, we define a multi-scale feature extraction block to extract and combine features at distinct scales of the noisy image. In addition, we propose a normalized attention network (NAN) to learn the relationships between channels, which smooths the optimization landscape and speeds up the convergence process for training an attention model. Moreover, we introduce the NAN to convolutional network denoising, in which each channel gets gain; channels can play different roles in the subsequent convolution. To testify the effectiveness of the proposed MFENANN, we used both grayscale and color image sets whose noise levels ranged from 0 to 75 to do the experiments. The experimental results show that compared with some state-of-the-art denoising methods, the restored images of MFENANN have larger peak signal-to-noise ratios (PSNR) and structural similarity index measure (SSIM) values and get better overall appearance.

scholarly journals Chicken Image Segmentation via Multi-Scale Attention-Based Deep Convolutional Neural Network

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Wei Li ◽  
Yang Xiao ◽  
Xibin Song ◽  
Na Lv ◽  
Xinbo Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Multi Scale
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