Research Article
Low Resolution 3D Image Enhancement Based on Artificial Neural Network
@INPROCEEDINGS{10.1007/978-3-031-50571-3_15, author={Yingjian Kang and Lei Ma and Jianxing Yang and Shufeng Zhuo}, title={Low Resolution 3D Image Enhancement Based on Artificial Neural Network}, proceedings={Multimedia Technology and Enhanced Learning. 5th EAI International Conference, ICMTEL 2023, Leicester, UK, April 28-29, 2023, Proceedings, Part I}, proceedings_a={ICMTEL}, year={2024}, month={2}, keywords={Artificial Neural Network Low Resolution 3D Image Image Enhancement Deblurring}, doi={10.1007/978-3-031-50571-3_15} }- Yingjian Kang
Lei Ma
Jianxing Yang
Shufeng Zhuo
Year: 2024
Low Resolution 3D Image Enhancement Based on Artificial Neural Network
ICMTEL
Springer
DOI: 10.1007/978-3-031-50571-3_15
Abstract
In order to improve the quality of low resolution 3D images, this study proposes a low resolution 3D image enhancement method based on artificial neural network. First of all, the adjustable filter is used to divide the image categories, and the multi-angle mesh model of the machine vision system is constructed. Then, the low resolution image is decomposed into multiple scales by filtering method. The white balance method is used to eliminate the color deviation of low resolution 3D images and realize the color correction of low resolution 3D images. Finally, the atmospheric scattering model is used to de blur the low resolution 3D image. Combining the advantages of color model transformation algorithm and artificial neural network, the low resolution 3D image enhancement algorithm is designed. Experimental results show that this method can improve the quality of low resolution 3D images and enhance the image enhancement effect.
