Digital Image Restoration Based on Multicontour Batch Scanning

Ding, Yongsheng; Yun-bo, Wei; Zhang, Shuisheng; Shi-hang, YU

doi:10.1155/2022/8106516

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…With the progress of information technology and the rapid development of image data acquisition technology, a large amount of multimedia data is generated every day in all walks of life, among which digital image data accounts for a large part [1][2][3][4][5][6]. Face with the explosive growth of digital image data, the traditional stand-alone mode of image processing has exposed many problems, such as slow processing speed and poor concurrency, etc.…”

Section: Introductionmentioning

confidence: 99%

Rapid Classification of Massive Images Based on Cloud Computing Platform

Wang¹,

Cao²

2023

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With the continuously increasing requirement of data processing speed, it is more and more suitable for images to be processed on cloud computing platform with distributed parallel computing. Therefore, this paper studied the rapid classification of massive images based on cloud computing platform. The multi-mode information fusion of massive images was carried out, which gave the implementation steps of the proposed massive image fusion method based on information complementation. This paper proposed to classify the massive images based on salient interactive feedback network. The model constructed in this paper not only simplified the complicated network parameter initialization and tuning process, but also avoided the classification failure caused by the scarcity of labeled images, which is more suitable for application and practical application scenarios. The experimental results verified the effectiveness of the constructed model in rapidly classifying massive images.

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Section: Introductionmentioning

confidence: 99%

Rapid Classification of Massive Images Based on Cloud Computing Platform

Wang¹,

Cao²

2023

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“…

This article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of systematic manipulation of the publication and peer-review process.

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confidence: 99%