scholarly journals CONVOLUTIONAL NEURAL NETWORK BASED DEM SUPER RESOLUTION

2016 ◽  
Vol XLI-B3 ◽  
pp. 247-250 ◽  
Author(s):  
Zixuan Chen ◽  
Xuewen Wang ◽  
Zekai Xu ◽  
Wenguang Hou
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Super Resolution ◽  
Low Resolution ◽  
Practical Applications ◽  
Interpolation Methods ◽  
New Strategy

DEM super resolution is proposed in our previous publication to improve the resolution for a DEM on basis of some learning examples. Meanwhile, the nonlocal algorithm is introduced to deal with it and lots of experiments show that the strategy is feasible. In our publication, the learning examples are defined as the partial original DEM and their related high measurements due to this way can avoid the incompatibility between the data to be processed and the learning examples. To further extent the applications of this new strategy, the learning examples should be diverse and easy to obtain. Yet, it may cause the problem of incompatibility and unrobustness. To overcome it, we intend to investigate a convolutional neural network based method. The input of the convolutional neural network is a low resolution DEM and the output is expected to be its high resolution one. A three layers model will be adopted. The first layer is used to detect some features from the input, the second integrates the detected features to some compressed ones and the final step transforms the compressed features as a new DEM. According to this designed structure, some learning DEMs will be taken to train it. Specifically, the designed network will be optimized by minimizing the error of the output and its expected high resolution DEM. In practical applications, a testing DEM will be input to the convolutional neural network and a super resolution will be obtained. Many experiments show that the CNN based method can obtain better reconstructions than many classic interpolation methods.

scholarly journals CONVOLUTIONAL NEURAL NETWORK BASED DEM SUPER RESOLUTION

2016 ◽  
Vol XLI-B3 ◽  
pp. 247-250 ◽  
Author(s):  
Zixuan Chen ◽  
Xuewen Wang ◽  
Zekai Xu ◽  
Wenguang Hou
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Super Resolution ◽  
Low Resolution ◽  
Practical Applications ◽  
Interpolation Methods ◽  
New Strategy

DEM super resolution is proposed in our previous publication to improve the resolution for a DEM on basis of some learning examples. Meanwhile, the nonlocal algorithm is introduced to deal with it and lots of experiments show that the strategy is feasible. In our publication, the learning examples are defined as the partial original DEM and their related high measurements due to this way can avoid the incompatibility between the data to be processed and the learning examples. To further extent the applications of this new strategy, the learning examples should be diverse and easy to obtain. Yet, it may cause the problem of incompatibility and unrobustness. To overcome it, we intend to investigate a convolutional neural network based method. The input of the convolutional neural network is a low resolution DEM and the output is expected to be its high resolution one. A three layers model will be adopted. The first layer is used to detect some features from the input, the second integrates the detected features to some compressed ones and the final step transforms the compressed features as a new DEM. According to this designed structure, some learning DEMs will be taken to train it. Specifically, the designed network will be optimized by minimizing the error of the output and its expected high resolution DEM. In practical applications, a testing DEM will be input to the convolutional neural network and a super resolution will be obtained. Many experiments show that the CNN based method can obtain better reconstructions than many classic interpolation methods.

scholarly journals Super-resolution reconstruction of seismic section image via multi-scale convolution neural network

2021 ◽  
Vol 303 ◽  
pp. 01058
Author(s):  
Meng-Di Deng ◽  
Rui-Sheng Jia ◽  
Hong-Mei Sun ◽  
Xing-Li Zhang
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Super Resolution ◽  
Image Features ◽  
Image Feature ◽  
Reconstruction Method ◽  
Low Resolution ◽  
Seismic Section ◽  
Multi Scale

The resolution of seismic section images can directly affect the subsequent interpretation of seismic data. In order to improve the spatial resolution of low-resolution seismic section images, a super-resolution reconstruction method based on multi-scale convolution is proposed. This method designs a multi-scale convolutional neural network to learn high-low resolution image feature pairs, and realizes mapping learning from low-resolution seismic section images to high-resolution seismic section images. This multi-scale convolutional neural network model consists of four convolutional layers and a sub-pixel convolutional layer. Convolution operations are used to learn abundant seismic section image features, and sub-pixel convolution layer is used to reconstruct high-resolution seismic section image. The experimental results show that the proposed method is superior to the comparison method in peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). In the total training time and reconstruction time, our method is about 22% less than the FSRCNN method and about 18% less than the ESPCN method.

scholarly journals Survey on CNN based super resolution methods

2021 ◽  
Vol 2 (4) ◽  
pp. 27-33
Author(s):  
Rafaa Amen Kazem ◽  
Jamila H. Suad ◽  
Huda Abdulaali Abdulbaqi
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Medical Images ◽  
Super Resolution ◽  
Low Resolution ◽  
Resolution Mapping ◽  
Super Resolution Mapping ◽  
Output Stages

Super Resolution is a field of image analysis that focuses on boosting the resolution of photographs and movies without compromising detail or visual appeal, instead enhancing both. Multiple (many input images and one output image) or single (one input and one output) stages are used to convert low-resolution photos to high-resolution photos. The study examines super-resolution methods based on a convolutional neural network (CNN) for super-resolution mapping at the sub-pixel level, as well as its primary characteristics and limitations for noisy or medical images.

scholarly journals Survey on CNN based super resolution methods

2021 ◽  
Vol 1 (4) ◽  
pp. 27-33
Author(s):  
Rafaa Amen Kazem ◽  
Jamila H. Suad ◽  
Huda Abdulaali Abdulbaqi
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Medical Images ◽  
Super Resolution ◽  
Low Resolution ◽  
Resolution Mapping ◽  
Super Resolution Mapping ◽  
Output Stages

Super Resolution is a field of image analysis that focuses on boosting the resolution of photographs and movies without compromising detail or visual appeal, instead enhancing both. Multiple (many input images and one output image) or single (one input and one output) stages are used to convert low-resolution photos to high-resolution photos. The study examines super-resolution methods based on a convolutional neural network (CNN) for super-resolution mapping at the sub-pixel level, as well as its primary characteristics and limitations for noisy or medical images.

scholarly journals Dynamic Convolutional Neural Network For Image Super-resolution

2018 ◽  
Vol 3 ◽  
pp. 1
Author(s):  
Anil Bhujel ◽  
Dibakar Raj Pant
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Super Resolution ◽  
Low Resolution ◽  
Scaling Factors ◽  
Resolution Image ◽  
High Resolution Image ◽  
Image Super Resolution ◽  
Up Scaling

<p>Single image super-resolution (SISR) is a technique that reconstructs high resolution image from single low resolution image. Dynamic Convolutional Neural Network (DCNN) is used here for the reconstruction of high resolution image from single low resolution image. It takes low resolution image as input and produce high resolution image as output for dynamic up-scaling factor 2, 3, and 4. The dynamic convolutional neural network directly learns an end-to-end mapping between low resolution and high resolution images. The CNN trained simultaneously with images up-scaled by factors 2, 3, and 4 to make it dynamic. The system is then tested for the input images with up-scaling factors 2, 3 and 4. The dynamically trained CNN performs well for all three up-scaling factors. The performance of network is measured by PSNR, WPSNR, SSIM, MSSSIM, and also by perceptual.</p><p><strong>Journal of Advanced College of Engineering and Management,</strong> Vol. 3, 2017, Page: 1-10</p>

scholarly journals Hyperspectral Super-Resolution Technique Using Histogram Matching and Endmember Optimization

2019 ◽  
Vol 9 (20) ◽  
pp. 4444
Author(s):  
Byunghyun Kim ◽  
Soojin Cho
Keyword(s):  
High Resolution ◽  
Spatial Information ◽  
Super Resolution ◽  
Spatial Mismatch ◽  
Low Resolution ◽  
Practical Applications ◽  
Histogram Matching ◽  
Shear Transformation ◽  
Entire Dataset ◽  
Rgb Image

In most hyperspectral super-resolution (HSR) methods, which are techniques used to improve the resolution of hyperspectral images (HSIs), the HSI and the target RGB image are assumed to have identical fields of view. However, because implementing these identical fields of view is difficult in practical applications, in this paper, we propose a HSR method that is applicable when an HSI and a target RGB image have different spatial information. The proposed HSR method first creates a low-resolution RGB image from a given HSI. Next, a histogram matching is performed on a high-resolution RGB image and a low-resolution RGB image obtained from an HSI. Finally, the proposed method optimizes endmember abundance of the high-resolution HSI towards the histogram-matched high-resolution RGB image. The entire procedure is evaluated using an open HSI dataset, the Harvard dataset, by adding spatial mismatch to the dataset. The spatial mismatch is implemented by shear transformation and cutting off the upper and left sides of the target RGB image. The proposed method achieved a lower error rate across the entire dataset, confirming its capability for super-resolution using images that have different fields of view.

Video super resolution using convolutional neural network and image fusion techniques

2021 ◽  
Vol 24 (4) ◽  
pp. 279-287
Author(s):  
Vikas Kumar ◽  
Tanupriya Choudhury ◽  
Suresh Chandra Satapathy ◽  
Ravi Tomar ◽  
Archit Aggarwal
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Image Fusion ◽  
Reference Model ◽  
Super Resolution ◽  
Motion Blur ◽  
Single Image ◽  
Image Super Resolution ◽  
Single Image Super Resolution

Recently, huge progress has been achieved in the field of single image super resolution which augments the resolution of images. The idea behind super resolution is to convert low-resolution images into high-resolution images. SRCNN (Single Resolution Convolutional Neural Network) was a huge improvement over the existing methods of single-image super resolution. However, video super-resolution, despite being an active field of research, is yet to benefit from deep learning. Using still images and videos downloaded from various sources, we explore the possibility of using SRCNN along with image fusion techniques (minima, maxima, average, PCA, DWT) to improve over existing video super resolution methods. Video Super-Resolution has inherent difficulties such as unexpected motion, blur and noise. We propose Video Super Resolution – Image Fusion (VSR-IF) architecture which utilizes information from multiple frames to produce a single high- resolution frame for a video. We use SRCNN as a reference model to obtain high resolution adjacent frames and use a concatenation layer to group those frames into a single frame. Since, our method is data-driven and requires only minimal initial training, it is faster than other video super resolution methods. After testing our program, we find that our technique shows a significant improvement over SCRNN and other single image and frame super resolution techniques.

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.

scholarly journals Dual Features Extraction Network for Image Super-Resolution

2021 ◽  
Author(s):  
Guosheng Zhao ◽  
Kun Wang
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Super Resolution ◽  
Features Extraction ◽  
Experimental Results ◽  
Actual Situation ◽  
Single Image ◽  
Image Super Resolution ◽  
Single Image Super Resolution

With the development of deep convolutional neural network, recent research on single image super-resolution (SISR) has achieved great achievements. In particular, the networks, which fully utilize features, achieve a better performance. In this paper, we propose an image super-resolution dual features extraction network (SRDFN). Our method uses the dual features extraction blocks (DFBs) to extract and combine low-resolution features, with less noise but less detail, and high-resolution features, with more detail but more noise. The output of DFB contains the advantages of low- and high-resolution features, with more detail and less noise. Moreover, due to that the number of DFB and channels can be set by weighting accuracy against size of model, SRDFN can be designed according to actual situation. The experimental results demonstrate that the proposed SRDFN performs well in comparison with the state-of-the-art methods.

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