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 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 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 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 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>

Learning depth super-resolution by using multi-scale convolutional neural network

2019 ◽  
Vol 36 (2) ◽  
pp. 1773-1783 ◽  
Author(s):  
Masoumeh Zareapoor ◽  
Pourya Shamsolmoali ◽  
Jie Yang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Super Resolution ◽  
Multi Scale

scholarly journals A Multi-Scale Mapping Approach Based on a Deep Learning CNN Model for Reconstructing High-Resolution Urban DEMs

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1369
Author(s):  
Ling Jiang ◽  
Yang Hu ◽  
Xilin Xia ◽  
Qiuhua Liang ◽  
Andrea Soltoggio ◽  
...  
Keyword(s):  
High Resolution ◽  
Accuracy Assessment ◽  
Cost Effective ◽  
Reconstruction Method ◽  
Low Resolution ◽  
Multi Scale ◽  
Mapping Approach ◽  
Assessment Approach ◽  
Urban Topography ◽  
Complex Features

The scarcity of high-resolution urban digital elevation model (DEM) datasets, particularly in certain developing countries, has posed a challenge for many water-related applications such as flood risk management. A solution to address this is to develop effective approaches to reconstruct high-resolution DEMs from their low-resolution equivalents that are more widely available. However, the current high-resolution DEM reconstruction approaches mainly focus on natural topography. Few attempts have been made for urban topography, which is typically an integration of complex artificial and natural features. This study proposed a novel multi-scale mapping approach based on convolutional neural network (CNN) to deal with the complex features of urban topography and to reconstruct high-resolution urban DEMs. The proposed multi-scale CNN model was firstly trained using urban DEMs that contained topographic features at different resolutions, and then used to reconstruct the urban DEM at a specified (high) resolution from a low-resolution equivalent. A two-level accuracy assessment approach was also designed to evaluate the performance of the proposed urban DEM reconstruction method, in terms of numerical accuracy and morphological accuracy. The proposed DEM reconstruction approach was applied to a 121 km2 urbanized area in London, United Kingdom. Compared with other commonly used methods, the current CNN-based approach produced superior results, providing a cost-effective innovative method to acquire high-resolution DEMs in other data-scarce regions.

scholarly journals Super Resolution pada Citra Udara menggunakan Convolutional Neural Network

2021 ◽  
Vol 9 (1) ◽  
pp. 71
Author(s):  
MUHAMMAD EFAN ABDULFATTAH ◽  
LEDYA NOVAMIZANTI ◽  
SYAMSUL RIZAL
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Unmanned Aerial Vehicle ◽  
Super Resolution ◽  
Image Features ◽  
Scale Factor ◽  
Aerial Imagery ◽  
Aerial Vehicle

ABSTRAKBencana di Indonesia didominasi oleh bencana hidrometeorologi yang mengakibatkan kerusakan dalam skala besar. Melalui pemetaan, penanganan yang menyeluruh dapat dilakukan guna membantu analisa dan penindakan selanjutnya. Unmanned Aerial Vehicle (UAV) dapat digunakan sebagai alat bantu pemetaan dari udara. Namun, karena faktor kamera maupun perangkat pengolah citra yang tidak memenuhi spesifikasi, hasilnya menjadi kurang informatif. Penelitian ini mengusulkan Super Resolution pada citra udara berbasis Convolutional Neural Network (CNN) dengan model DCSCN. Model terdiri atas Feature Extraction Network untuk mengekstraksi ciri citra, dan Reconstruction Network untuk merekonstruksi citra. Performa DCSCN dibandingkan dengan Super Resolution CNN (SRCNN). Eksperimen dilakukan pada dataset Set5 dengan nilai scale factor 2, 3 dan 4. Secara berurutan SRCNN menghasilkan nilai PSNR dan SSIM sebesar 36.66 dB / 0.9542, 32.75 dB / 0.9090 dan 30.49 dB / 0.8628. Performa DCSCN meningkat menjadi 37.614dB / 0.9588, 33.86 dB / 0.9225 dan 31.48 dB / 0.8851.Kata kunci: citra udara, deep learning, super resolution ABSTRACTDisasters in Indonesia are dominated by hydrometeorological disasters, which cause large-scale damage. Through mapping, comprehensive handling can be done to help the analysis and subsequent action. Unmanned Aerial Vehicle (UAV) can be used as an aerial mapping tool. However, due to the camera and image processing devices that do not meet specifications, the results are less informative. This research proposes Super Resolution on aerial imagery based on Convolutional Neural Network (CNN) with the DCSCN model. The model consists of Feature Extraction Network for extracting image features and Reconstruction Network for reconstructing images. DCSCN's performance is compared to CNN Super Resolution (SRCNN). Experiments were carried out on the Set5 dataset with scale factor values 2, 3, and 4. The SRCNN sequentially produced PSNR and SSIM values of 36.66dB / 0.9542, 32.75dB / 0.9090 and 30.49dB / 0.8628. DCSCN's performance increased to 37,614dB / 0.9588, 33.86dB / 0.9225 and 31.48dB / 0.8851.Keywords: aerial imagery, deep learning, super resolution

scholarly journals Cascaded SR-GAN for Scale-Adaptive Low Resolution Person Re-identification

2018 ◽  
Author(s):  
Zheng Wang ◽  
Mang Ye ◽  
Fan Yang ◽  
Xiang Bai ◽  
Shin'ichi Satoh
Keyword(s):  
High Resolution ◽  
Super Resolution ◽  
Feature Representation ◽  
Image Feature ◽  
Low Resolution ◽  
Open World ◽  
In Series ◽  
High Resolution Images ◽  
Public Dataset ◽  
Image Super Resolution

Person re-identification (REID) is an important task in video surveillance and forensics applications. Most of previous approaches are based on a key assumption that all person images have uniform and sufficiently high resolutions. Actually, various low-resolutions and scale mismatching always exist in open world REID. We name this kind of problem as Scale-Adaptive Low Resolution Person Re-identification (SALR-REID). The most intuitive way to address this problem is to increase various low-resolutions (not only low, but also with different scales) to a uniform high-resolution. SR-GAN is one of the most competitive image super-resolution deep networks, designed with a fixed upscaling factor. However, it is still not suitable for SALR-REID task, which requires a network not only synthesizing high-resolution images with different upscaling factors, but also extracting discriminative image feature for judging person’s identity. (1) To promote the ability of scale-adaptive upscaling, we cascade multiple SRGANs in series. (2) To supplement the ability of image feature representation, we plug-in a reidentification network. With a unified formulation, a Cascaded Super-Resolution GAN (CSR-GAN) framework is proposed. Extensive evaluations on two simulated datasets and one public dataset demonstrate the advantages of our method over related state-of-the-art methods.

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