An Image Super-Resolution Reconstruction Method with Single Frame Character Based on Wavelet Neural Network in Internet of Things

Guo, Lingli; Woźniak, Marcin

doi:10.1007/s11036-020-01681-6

Cited by 24 publications

(9 citation statements)

References 22 publications

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“…Network Algorithm. Fuzzy neural network is the product of combining fuzzy theory with neural network, which brings together the advantages of neural network and fuzzy theory and integrates learning, association, recognition, and information processing [24][25][26][27][28][29][30][31].…”

Section: Research Design Of Music Teaching Assessment Model In Colleges and Universities Under Compensated Fuzzy Neuralmentioning

confidence: 99%

[Retracted] Compensated Fuzzy Neural Network‐Based Music Teaching Ability Assessment Model

Chen

2021

Computational Intelligence and Neuroscience

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College is the main place to carry out music teaching, and it is important to assess the music teaching ability in college effectively. Based on this, this paper firstly analyzes the necessity of music teaching ability assessment and briefly summarizes the application of neural network and deep learning technology in music teaching ability assessment and secondly designs an assessment model based on compensated fuzzy neural network algorithm and analyzes the accuracy of the model, finds out the causes of forming abnormal output by analysing the general dimensional conditions of the algorithm of the model, and proposes corresponding correction. Finally, the reliability and feasibility of the music teaching ability assessment model were experimentally verified by combining with teaching practice. The research results confirm the feasibility of the compensated fuzzy neural network algorithm in music teaching ability assessment, which has important reference significance for improving the quality of music teaching in colleges and universities.

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Section: Research Design Of Music Teaching Assessment Model In Colleges and Universities Under Compensated Fuzzy Neuralmentioning

confidence: 99%

[Retracted] Compensated Fuzzy Neural Network‐Based Music Teaching Ability Assessment Model

Chen

2021

Computational Intelligence and Neuroscience

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“…e development of the deep learning algorithm is also at the forefront of the world [23]. Many researchers mainly analyse the performance of the deep learning algorithm and shallow neural network algorithm in the research of digital recognition [24][25][26][27][28]. Subsequently, it is confirmed that the deep belief network has great applicability and high efficiency.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Research on Information Visualization Graphic Design Teaching Based on DBN Algorithm

Xue

2021

Computational Intelligence and Neuroscience

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With the advent of the era of big data, how to quickly obtain effective information and efficiently disseminate information technology has become the most popular topic. Studies have shown that the ability of the human brain to process data and information is unmatched by machines, and the processing of graphics is tens of thousands of times faster than that of words. Based on the deep belief network (DBN) algorithm, this paper studies the technology of information visualization graphic design teaching application. Firstly, the structure of the deep belief network is analysed to explore its technical application in graphic information reconstruction. It is concluded that the DBN algorithm can be used to deal with the problems of classification, regression, dimension calculation, feature point acquisition, accuracy calculation, and so on in machine learning training. Then, the deformation technology of graphic local design is studied based on the DBN algorithm to construct the visual teaching platform and analyse the technical research results of this algorithm in information graphic design. The results show that the DBN algorithm can quickly solve the problem of processing complex features in graphics, change the local deformation design of the original graphics to form new feature point data and add it to the teaching platform, and improve the ability of model fast learning and training, optimizing the operation efficiency of the teaching platform.

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“…In the image processing field, multi-focus-image-fusion is a significant branch [ 1 , 2 , 3 ]. Multi-focus-image-fusion is the process of combining two or more images with different focal points of the same scene into a composite image with all-focus, which is of service to humans and machine perception [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

The Multi-Focus-Image-Fusion Method Based on Convolutional Neural Network and Sparse Representation

Feng

Wang

Gao

2021

Entropy

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Multi-focus-image-fusion is a crucial embranchment of image processing. Many methods have been developed from different perspectives to solve this problem. Among them, the sparse representation (SR)-based and convolutional neural network (CNN)-based fusion methods have been widely used. Fusing the source image patches, the SR-based model is essentially a local method with a nonlinear fusion rule. On the other hand, the direct mapping between the source images follows the decision map which is learned via CNN. The fusion is a global one with a linear fusion rule. Combining the advantages of the above two methods, a novel fusion method that applies CNN to assist SR is proposed for the purpose of gaining a fused image with more precise and abundant information. In the proposed method, source image patches were fused based on SR and the new weight obtained by CNN. Experimental results demonstrate that the proposed method clearly outperforms existing state-of-the-art methods in addition to SR and CNN in terms of both visual perception and objective evaluation metrics, and the computational complexity is greatly reduced. Experimental results demonstrate that the proposed method not only clearly outperforms the SR and CNN methods in terms of visual perception and objective evaluation indicators, but is also significantly better than other state-of-the-art methods since our computational complexity is greatly reduced.

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An Image Super-Resolution Reconstruction Method with Single Frame Character Based on Wavelet Neural Network in Internet of Things

Cited by 24 publications

References 22 publications

[Retracted] Compensated Fuzzy Neural Network‐Based Music Teaching Ability Assessment Model

[Retracted] Compensated Fuzzy Neural Network‐Based Music Teaching Ability Assessment Model

[Retracted] Research on Information Visualization Graphic Design Teaching Based on DBN Algorithm

The Multi-Focus-Image-Fusion Method Based on Convolutional Neural Network and Sparse Representation

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