Author(s)

Fan Dai ¹

Affiliation(s)

¹ School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China

Corresponding Author

Fan Dai

ABSTRACT

With the rapid development of deep learning technology, Region-based Convolutional Neural Network (RCNN) is a milestone method in the field of object detection, and its powerful feature extraction and recognition capabilities have brought new breakthroughs to image inpainting technology. This paper analyzes the application of RCNN in the field of image restoration in detail, and discusses its principles, advantages, and challenges in practical applications.

KEYWORDS

RCNN, image restoration, deep learning, object detection

CITE THIS PAPER

Fan Dai, Analysis of image restoration technology under RCNN. Advances in Computer, Signals and Systems (2024) Vol. 8: 129-133. DOI: http://dx.doi.org/10.23977/acss.2024.080219.

REFERENCES

[1] Huo Xingyu, Che Ying. (2023). Image Restoration Technology Based on RCNN [J]. Journal of Changchun University of Science and Technology (Natural Science Edition), 46(04), 107-113.
[2] Huang Zhifei. (2023). Research on Image Restoration and Detection of Meat Products Based on Hyperspectral Technology [D]. Northeast Electric Power University.
[3] Ni Yuanyuan. (2023). Research on Image Restoration Technology Based on Cross-Scale Attention Mechanism [D]. North China Electric Power University (Beijing).
[4] Zhang Zelong. (2023). Research on Image Restoration Technology Based on Multi-Scale Feature Fusion [D]. University of Electronic Science and Technology of China.
[5] Shi Jiajun, Peng Bin, Xu Jinfu. (2023). Application of Pixel Restoration Technology Based on Near-Field Dynamics Differential Operator in Image Restoration of Masonry Structure [J]. Journal of Civil Engineering and Management, 40(01), 109-113.
[6] Wang Lin. (2022). Research on Image Restoration Algorithm Based on Adversarial Generation Technology and Biomedical Image Application [D]. Zhejiang Ocean University.

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