Image Enhancement Research System Based on OpenCV

Authors

  • Jinhua Li

DOI:

https://doi.org/10.54097/53thtd69

Keywords:

Image enhancement; Convolutional neural network; openCV; System design.

Abstract

As a carrier of information, image analysis and processing are indispensable in the construction of smart cities. For example, object detection, tracking and recognition in the study of automatic driving all rely on external input images. Therefore, high-quality images play an important role in the era of intelligence, and the study of image enhancement has very high practical significance. In this paper, the convolutional neural network is designed for feature extraction to achieve fuzzy image enhancement, and the image enhancement research system is designed based on openCV and Qt.

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References

Liu Wankui, Liu Yue.Review on illumination estimation in augmented reality [J]. Journal of Computer-Aided Design & Computer Graphics, 2016, 28 (2): 197-207.

Wang Ping, Sun Zhenming.Multi-level decomposition retinex low-light image enhancement algorithm [J]. Application Research of Computers, 2020, 37 (4): 1204-1209.

Huang Hui, Dong Linlu, Liu Xiaofang, et al. Improved retinex low light image enhancement method [J]. Optics and Precision Engineering, 2020, 28 (8): 1835-1849

Mao xingYun. Introduction to OpenCV3 programming [M]. Beijing: Publishing House of Electronics Industry, 2015..

Blanchette J, Summerfield M.C++ GUI Programming with Qt 4[M].America:Prentice Hall Press, 2006

Lu Wenzhou.Qt5 Development and Practice [M]. 2nd Ed., Beijing: Publishing House of Electronics Industry, 2015.

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Published

23-02-2024

Issue

Section

Articles

How to Cite

Li, J. (2024). Image Enhancement Research System Based on OpenCV. Academic Journal of Science and Technology, 9(2), 162-164. https://doi.org/10.54097/53thtd69