| Title |
The artistic image processing for visual healing in smart city |
| ID_Doc |
36595 |
| Authors |
Qu, GF; Song, Q; Fang, T |
| Title |
The artistic image processing for visual healing in smart city |
| Year |
2024 |
| Published |
Scientific Reports, 14, 1 |
| DOI |
10.1038/s41598-024-68082-7 |
| Abstract |
This study investigates the processing methods of artistic images within the context of Smart city (SC) initiatives, focusing on the visual healing effects of artistic image processing to enhance urban residents' mental health and quality of life. Firstly, it examines the role of artistic image processing techniques in visual healing. Secondly, deep learning technology is introduced and improved, proposing the overlapping segmentation vision transformer (OSViT) for image blocks, and further integrating the bidirectional long short-term memory (BiLSTM) algorithm. An innovative artistic image processing and classification recognition model based on OSViT-BiLSTM is then constructed. Finally, the visual healing effect of the processed art images in different scenes is analyzed. The results demonstrate that the proposed model achieves a classification recognition accuracy of 92.9% for art images, which is at least 6.9% higher than that of other existing model algorithms. Additionally, over 90% of users report satisfaction with the visual healing effects of the artistic images. Therefore, it is found that the proposed model can accurately identify artistic images, enhance their beauty and artistry, and improve the visual healing effect. This study provides an experimental reference for incorporating visual healing into SC initiatives. |
| Author Keywords |
Smart city; Deep learning; Visual healing; Artistic image; Visual attention model |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001274726000089 |
| WoS Category |
Multidisciplinary Sciences |
| Research Area |
Science & Technology - Other Topics |
| PDF |
https://www.nature.com/articles/s41598-024-68082-7.pdf
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