Residual learning of deep convolutional neural networks for image denoising

Shan, Chuanhui; Guo, Xirong; Ou, Jun

doi:10.3233/jifs-190017

Cited by 15 publications

(6 citation statements)

References 22 publications

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“…Shan et al used feature-based technology to extract the feature size required for clothing from 3D human model data. A 3D human body modeling and clothing has always been a hot and difficult point in the field of computer graphics and CAD clothing [9]. For a long time, research in this field mainly formed the following modeling methods: use points, lines, and curves to build a 3D wireframe model, and use voxels to build a 3D solid model; use points, edges, and surfaces to build a 3D surface model, using the mesh facet method; and the 3D surface model is built on the basis of 3D physical modeling.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Convolutional Neural Network Algorithm under Deep Learning in Digital Clothing Design

Xue

2022

Security and Communication Networks

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In order to overcome the influence of background, lighting, deformation, and other factors, using a constitutional neural network structure combined with metric learning, specifically, it includes two model structures, Siamese, and Triplet. The use of bicubic NURBS surfaces is proposed, the idea of constructing mannequins and garment pieces, the experimental results show that NURBS surface control is flexible and simple, and the calculation is stable, and it is the best surface for constructing virtual samples and avatars. Based on studying the three-dimensional structure design of clothing, based on the 10 key curves of the human body, the curve and surface interpolation algorithm is applied, by calling OpenGL related functions, the establishment of the benchmark human body model is well realized, and it lays a foundation for the deformation of the human body model based on parameters in the future.

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Section: Literature Reviewmentioning

confidence: 99%

Application of Convolutional Neural Network Algorithm under Deep Learning in Digital Clothing Design

Xue

2022

Security and Communication Networks

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“…The image features are then described to obtain a statistical histogram, and with intersection kernel SVM, a linear combination is made between the probabilities of occurrence of different angles, and the highest probability value after the combination is taken as the recognition result [20], and the algorithmic procedure is shown in…”

Section: Feature Recognition and Correctionmentioning

confidence: 99%

“…In equation (20), ðς 1 , ς 2 , ⋯, ς η Þ and ðς 1 ′ , ς 2 ′ , ⋯, ς η ′ Þ represent the eigenvectors of the two feature points.…”

Section: Feature Recognition and Correctionmentioning

confidence: 99%

“…Assume that the ball speed is 80 km/h and the height of the hitting point is about 3.2 m. Firstly, 10 images of players' spike shot angles are acquired using Gabor filter and feature extraction is performed using an 8 × 8 grid. Before feature extraction, image denoising is performed using the method in this paper, 3D shear domain image denoising in the literature [19], and convolutional neural network-based denoising method in the literature [20]. The final image signal-to-noise ratio obtained is shown in Figure 4.…”

Section: Simulation Experimental Data Analysis and Researchmentioning

confidence: 99%

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Automatic Recognition and Correction of Volleyball Players’ Release Angle Based on Feature Statistics

Rui

2021

Journal of Sensors

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The constant reform of the competition rules has promoted the innovation of volleyball techniques and tactics. In order to improve the training efficiency and competitive level of volleyball players, this study designed a volleyball player shooting angle automatic recognition and correction method based on the process of feature statistics. Firstly, the basic structure of the information acquisition system is analyzed, and the acquisition process is determined. Then, grayscale and binarization operations are carried out for color-moving images to separate their foreground and background, and a median filtering algorithm is used to remove the image noise. Then, the image pyramid of different sizes is generated by the filter. Based on setting the datum direction, the feature of volleyball shooting is extracted by using the line formula. On this basis, we construct a support vector machine (SVM) classifier to statistically classify the features, use the histogram additive kernel support vector machine method to obtain the lens angle recognition results, and correct the lens angle through feature point matching. Simulation experiments show that this method can effectively remove image noise and make the image signal-to-noise ratio higher, and it can effectively identify whether volleyball players’ release Angle is correct, to achieve the purpose of timely correction.

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“…(b). e details of the proposed ResNet residual neural network are introduced as follows[32,37]. Input layer: the inputs of the ResNet are color images of the real scenes, and the size of the input image is 224 * 224 * 3.…”

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confidence: 99%

An Improved Deep Residual Network-Based Semantic Simultaneous Localization and Mapping Method for Monocular Vision Robot

Gong

et al. 2020

Computational Intelligence and Neuroscience

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The robot simultaneous localization and mapping (SLAM) is a very important and useful technology in the robotic field. However, the environmental map constructed by the traditional visual SLAM method contains little semantic information, which cannot satisfy the needs of complex applications. The semantic map can deal with this problem efficiently, which has become a research hot spot. This paper proposed an improved deep residual network- (ResNet-) based semantic SLAM method for monocular vision robots. In the proposed approach, an improved image matching algorithm based on feature points is presented, to enhance the anti-interference ability of the algorithm. Then, the robust feature point extraction method is adopted in the front-end module of the SLAM system, which can effectively reduce the probability of camera tracking loss. In addition, the improved key frame insertion method is introduced in the visual SLAM system to enhance the stability of the system during the turning and moving of the robot. Furthermore, an improved ResNet model is proposed to extract the semantic information of the environment to complete the construction of the semantic map of the environment. Finally, various experiments are conducted and the results show that the proposed method is effective.

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Residual learning of deep convolutional neural networks for image denoising

Cited by 15 publications

References 22 publications

Application of Convolutional Neural Network Algorithm under Deep Learning in Digital Clothing Design

Application of Convolutional Neural Network Algorithm under Deep Learning in Digital Clothing Design

Automatic Recognition and Correction of Volleyball Players’ Release Angle Based on Feature Statistics

An Improved Deep Residual Network-Based Semantic Simultaneous Localization and Mapping Method for Monocular Vision Robot

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