scholarly journals Large receptive field networks for accurate high-scale image super-resolution

2021 ◽  
Author(s):  
George Seif
Keyword(s):  
Neural Network ◽  
Receptive Field ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Super Resolution ◽  
Neural Network Architecture ◽  
High Scale ◽  
One Dimensional ◽  
Image Super Resolution ◽  
And Performance

This thesis presents a novel convolutional neural network architecture for high-scale image super-resolution. In particular, we introduce two separate modifications that can be made to the convolutional layers in the network: one-dimensional kernels and dilated kernels. We show how both of these methods can be used to expand the receptive field and performance of super-resolution networks, without increasing the number of trainable parameters or network depth. We show that these modifications can easily be integrated into any convolutional neural network to improve performance. Our methods are especially effective for the challenging high scale super-resolution due to the expanded network receptive field. We conduct extensive empirical evaluations to demonstrate the effectiveness of our methods, showing strong improvements over the state-of-the-art.

scholarly journals Large receptive field networks for accurate high-scale image super-resolution

2021 ◽  
Author(s):  
George Seif
Keyword(s):  
Neural Network ◽  
Receptive Field ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Super Resolution ◽  
Neural Network Architecture ◽  
High Scale ◽  
One Dimensional ◽  
Image Super Resolution ◽  
And Performance

This thesis presents a novel convolutional neural network architecture for high-scale image super-resolution. In particular, we introduce two separate modifications that can be made to the convolutional layers in the network: one-dimensional kernels and dilated kernels. We show how both of these methods can be used to expand the receptive field and performance of super-resolution networks, without increasing the number of trainable parameters or network depth. We show that these modifications can easily be integrated into any convolutional neural network to improve performance. Our methods are especially effective for the challenging high scale super-resolution due to the expanded network receptive field. We conduct extensive empirical evaluations to demonstrate the effectiveness of our methods, showing strong improvements over the state-of-the-art.

scholarly journals Fast and Efficient Edge Fusing Network Architectures for Accurate Single Image Super-resolution

2021 ◽  
Author(s):  
Debjoy Chowdhury
Keyword(s):  
Neural Network ◽  
Receptive Field ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Performance Enhancement ◽  
Super Resolution ◽  
Main Concept ◽  
High Scale ◽  
Image Super Resolution ◽  
And Performance

Recovering a High-Resolution (HR) image from a Low-Resolution (LR) image is the main concept of image Super-Resolution (SR). Convolution Neural Networks (CNN) are becoming widely adopted in many applications including generation of HR images from LR images. Although CNNs are widely used with great performance improvements, there is still much room for improvement. There has always been a trade-off between the number of parameters and performance enhancement. This thesis presents a novel convolutional neural network architecture for high scale image SR inspired by the DenseNet and ResNet architecture. In particular, modifications can be made to the convolutional layers in the network: stacking the features and reusing the weight layers to increase the receptive field. It is shown how this method can be used to expand the receptive field and performance of super-resolution networks, without increasing the number of trainable parameters and sacrificing the computation time. These modifications can easily be integrated into any convolutional neural network to improve the accuracy by efficient high-level feature extraction while reducing training time and parameter numbers. Proposed methods are especially effective for the challenging high scale SR due to edge and texture recovery through the expanded network receptive field. Experimental results show that the proposed model outperforms the state-of-the-art methods.

scholarly journals Fast and Efficient Edge Fusing Network Architectures for Accurate Single Image Super-resolution

2021 ◽  
Author(s):  
Debjoy Chowdhury
Keyword(s):  
Neural Network ◽  
Receptive Field ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Performance Enhancement ◽  
Super Resolution ◽  
Main Concept ◽  
High Scale ◽  
Image Super Resolution ◽  
And Performance

Recovering a High-Resolution (HR) image from a Low-Resolution (LR) image is the main concept of image Super-Resolution (SR). Convolution Neural Networks (CNN) are becoming widely adopted in many applications including generation of HR images from LR images. Although CNNs are widely used with great performance improvements, there is still much room for improvement. There has always been a trade-off between the number of parameters and performance enhancement. This thesis presents a novel convolutional neural network architecture for high scale image SR inspired by the DenseNet and ResNet architecture. In particular, modifications can be made to the convolutional layers in the network: stacking the features and reusing the weight layers to increase the receptive field. It is shown how this method can be used to expand the receptive field and performance of super-resolution networks, without increasing the number of trainable parameters and sacrificing the computation time. These modifications can easily be integrated into any convolutional neural network to improve the accuracy by efficient high-level feature extraction while reducing training time and parameter numbers. Proposed methods are especially effective for the challenging high scale SR due to edge and texture recovery through the expanded network receptive field. Experimental results show that the proposed model outperforms the state-of-the-art methods.

scholarly journals Image Super-Resolution for MRI Images using 3D Faster Super-Resolution Convolutional Neural Network architecture

2020 ◽  
Vol 32 ◽  
pp. 03044
Author(s):  
Vanita Mane ◽  
Suchit Jadhav ◽  
Praneya Lal
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Brain Mri ◽  
Super Resolution ◽  
Structural Mri ◽  
Reconstruction Performance ◽  
Human Connectome Project ◽  
Image Super Resolution

Single image super-resolution using deep learning techniques has shown very high reconstruction performance over the last few years. We propose a novel three-dimensional convolutional neural network called 3D FSRCNN based on FSRCNN, which reinstates the high-resolution quality of structural MRI. The 3D neural network generates output brain images of high-resolution (HR) from a low-resolution (LR) input image. A simple design ensures less time complexity and high reconstruction quality. The network is trained using T1-weighted structural MRI images from the human connectome project dataset which is a large publicly available brain MRI database.

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