Single Image Super-Resolution Reconstruction based on the ResNeXt Network

Wang

Mathematical Problems in Engineering

et al. 2021

In the fault classification and identification of flexible DC transmission lines, it is inevitable to use the voltage and current characteristics of the transmission line. All kinds of data transformation methods can highlight the hidden characteristics of the original fault electrical quantity. Various artificial intelligence algorithms can further reduce the difficulty of transmission line fault classification. For such fault classification methods, this paper first builds a four-terminal flexible direct current transmission system model on PSCAD/EMTDC platform and obtains data by simulating different faults of transmission lines. Then, empirical mode decomposition (EMD), wavelet transform (WT), fast Fourier transform (FFT), and variational mode decomposition (VMD) are performed on the obtained data, respectively. Finally, the transformed data and original data are used as inputs to classify by convolutional neural network (CNN). The influence of one data transformation method and different combinations of two data transformation methods on CNN classification results is explored. The simulation results show that when only one data transformation method is used, CNN has the best classification effect for the data after VMD transformation. The classification accuracy and recall rate are both increased from 96.9% and 96.3% without data transformation to 99.88%. When VMD and FFT are combined, CNN classification results’ accuracy and recall rate are further improved to 99.96%.

Section: Cnn Structure Of Fault Classificationmentioning

confidence: 99%

Comparison and Analysis of the Influence of Different Data Transformation Methods on the Fault Identification of Flexible DC Transmission Lines by Convolutional Neural Network

Wang

Mathematical Problems in Engineering

et al. 2021

“…In the evaluation of hyper-segmentation image effects, [24] professionals are required to judge the image quality, which is heavily subjective, making a unified evaluation standard difficult. Image quality is judged by metrics [25][26] such as the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), whose higher values in-dicate less image distortion and higher image quality. PSNR is calculated as…”

Section: Common Evaluation Indicatorsmentioning

confidence: 99%

A Super-resolution Reconstruction Method for Underwater Fish Images with Feedback Networks

Wang¹,

Wang²,

Mao³

et al. 2022

Journal of Compurters

<p>Video surveillance is commonly used for production process monitoring in aquaculture. Because underwater video images are prone to blurring and unclear detail texture, this paper proposes a super-resolution network reconstruction method with a feedback network. The reconstruction effect is enhanced by a feedback network in the process of mapping low-resolution images to high-resolution images, which is implemented with a constrained recurrent neural network (RNN) to process deep feedback information. However, the shallow features are fed to the feedback net-work module to generate deeper features through multiple upsampling and downsampling and progressive refinement, which give the network structure an early reconstruction capability and are conducive to more realistically reconstructing high-resolution images. A course learning strategy is introduced to make the network applicable to more complex tasks and improve its robust-ness. We construct a training set of 800 images and validation and test sets of 10 images each using images of marine net fish as the research object. The method of this paper is validated on the public dataset Set14 and a self-built dataset. The proposed method outperforms other methods in both subjective and objective evaluations on the public and self-built datasets. Good foundation for high-definition monitoring of aquaculture is laid. </p> <p> </p>

“…To solve the complex computation, unstable network and slow learning speed problems of a generative adversarial network for image super-resolution (SRGAN), Zeng et al proposed a single image super-resolution reconstruction model called the Res_WGAN based on ResNeXt in their contribution "Single image super-resolution reconstruction based on the ResNeXt network" [9]. The generator is constructed by the ResNeXt network, which reduced the computational complexity of the model generator to 1/8 that of the SRGAN.…”

Section: Applicationsmentioning

confidence: 99%

Artificial intelligence in deep learning algorithms for multimedia analysis

Jeon

Anisetti

Damiani

et al. 2020

Multimed Tools Appl

Deep learning has gained a lot of research interest in artificial intelligence (AI) in many applications, such as image understanding, object detection, feature extraction, audio/ video processing, image demosaicking and denoising, overhead views in industrial applications. In addition, exploitation of deep learning in the field of data science, particularly in big data analytics focuses on high-level feature extraction and abstraction as data representation based on the hierarchical learning process. Moreover, deep learning is also designed to tackle supervised learning problems for a wide variety of tasks. How to reliably solve unsupervised tasks with a similar degree of success is an important issue to address. Such studies are investigated based on the adoption of parallel computing, i.e., GPUs and CPUs clusters.Various algorithms are already applied to achieve the desired goals. For instance, convolutional neural network has established superior performance on large-scale image and video classification. The supervised learning (semi/weakly) methods have expressively enhanced the performance when only a small amount of annotated data is available. In addition, correlation analysis, transfer learning, multi-tasking have proven