Image rendering efficiency improvement based on deep autoencoder in virtual environment

Xinran Han; Xiaoliang Wu; Hanwen Zhang; Xin Huang; Hexun Wang; Xiaotang Sun

doi:10.1117/12.2673329

14 April 2023 Image rendering efficiency improvement based on deep autoencoder in virtual environment

Xinran Han, Xiaoliang Wu, Hanwen Zhang, Xin Huang, Hexun Wang, Xiaotang Sun

Proceedings Volume 12613, International Conference on Computer Vision, Application, and Algorithm (CVAA 2022); 126130W (2023) https://doi.org/10.1117/12.2673329
Event: International Conference on Computer Vision, Application, and Algorithm (CVAA 2022), 2022, Chongqing, China

Abstract

Aiming at the problem that traditional image rendering methods are time-consuming and complex and cannot meet the application scenarios of modern design. In this paper, the depth self-encoder and capsule network in artificial intelligence (AI) technology are used to study the automatic rendering of images. Firstly, Scharr filter is used to reconstruct the image in Stack Capsule Autoencoder (SCAE) model to enhance the accuracy of image target detection and reduce the loss of reconstructed image. Then the loss function of the model is improved. Finally, in the Modified National Institute of Standards and Technology (MNIST) data set, Canadian Institute for Advanced Research-10 (CIFAR-10) data set, the performance of the improved model is tested on the Canadian Institute for Advanced Research-100 (CIFAR-100) data set and the data set made by the author. The test results show that the accuracy of the improved model is higher than that of the traditional K-means, Autoencoder network (AE) unsupervised algorithm and the improved depth clustering unsupervised model. The stacked capsule self-encoder using Scharr filter can make the classification effect of the model more accurate. The improved algorithm in this paper provides some references for improving the efficiency of image rendering in virtual environment.

Citation Download Citation

Xinran Han, Xiaoliang Wu, Hanwen Zhang, Xin Huang, Hexun Wang, and Xiaotang Sun "Image rendering efficiency improvement based on deep autoencoder in virtual environment", Proc. SPIE 12613, International Conference on Computer Vision, Application, and Algorithm (CVAA 2022), 126130W (14 April 2023); https://doi.org/10.1117/12.2673329

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KEYWORDS

Data modeling

Image enhancement

Image restoration

Visualization

Education and training

Performance modeling

Tunable filters

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