To enhance the efficiency and effectiveness of cultural heritage scene rendering, enabling dynamic interaction and immersive experiences for cultural inheritance. The study improves the grey wolf optimization algorithm, integrates it into deep learning oversampling training, and optimizes rendering based on color attribute theory. Performance is evaluated through function testing and image reconstruction, with examples from Dunhuang murals and the Forbidden City. The improved algorithm achieved fast convergence (50 iterations for uni-modal, 80 for multi-modal functions) and high robustness (98.6% success rate, 0.35 s runtime). Rendering optimization increased contrast and color saturation significantly, enhancing visual appeal and cultural conveyance. This research innovatively combines improved optimization algorithms with deep learning and color theory, offering an efficient technical solution for digital cultural heritage preservation and interactive display, promoting cultural diversity and heritage appreciation.
TytarenkoM. Optimizing immersion: analyzing graphics and performance considerations in Unity3D VR development. AJRCoS2023; 16(4): 104–114.
2.
TsipiLVouyioukasDLoumosG, et al.Digital repository as a service (D-RaaS): enhancing access and preservation of cultural heritage artifacts. Heritage2023; 6(10): 6881–6900.
3.
JinyiS. Intercultural visual communication design: cultural variances and harmonization. Adv Ind Eng Manag2023; 13(1): 7–12.
4.
AbbasiSLanHChoupanJ, et al.Deep learning for the harmonization of structural MRI scans: a survey. Biomed Eng Online2024; 23(1): 90–131.
5.
AbbasJYousefMPeledN, et al.Predictive factors for degenerative lumbar spinal stenosis: a model obtained from a machine learning algorithm technique. BMC Muscoskelet Disord2023; 24(1): 218–225.
6.
DaxiQZhijieLJunxiC, et al.The promotion of chaozhou culture based on the wechat platform under the 5w model. Adv Ind Eng Manag2024; 13(4): 7–12.
7.
KavehMMesgariMS. Application of meta-heuristic algorithms for training neural networks and deep learning architectures: a comprehensive review. Neural Process Lett2023; 55(4): 4519–4622.
8.
QiuYYangXChenS. An improved gray wolf optimization algorithm solving to functional optimization and engineering design problems. Sci Rep2024; 14(1): 14190–14213.
9.
ShialGSahooSPanigrahiS. An enhanced GWO algorithm with improved explorative search capability for global optimization and data clustering. Appl Artif Intell2023; 37(1): 2166232–2166282.
10.
OuYQinFZhouKQ, et al.An improved grey wolf optimizer with multi-strategies coverage in wireless sensor networks. Symmetry2024; 16(3): 286–304.
11.
ZhouKTanCZhaoY, et al.Research on solving flexible job shop scheduling problem based on improved GWO algorithm SS-GWO. Neural Process Lett2024; 56(1): 26–51.
12.
LeeMGChoHBYoumSK, et al.Detection of pine wilt disease using time series UAV imagery and deep learning semantic segmentation. Forests2023; 14(8): 1576–1587.
13.
ChithraPLAparnaR. Dual level security scheme (DLSS) using RGB layer cryptography and audio steganography for secret image transmission in unsecure medium. Indian J Sci Technol2023; 16(23): 1733–1744.
14.
HeSLiQLiX, et al.A lightweight convolutional neural network based on dynamic level‐set loss function for spine MR image segmentation. J Magn Reson Imag2024; 59(4): 1438–1453.
15.
DengFChenSMaY, et al.RCAU-Net: convolutional networks with residual channel attention for non-uniformity correction. Int J Sens Netw2024; 44(3): 169–181.
16.
FerrãoJDiasPSantosBS, et al.Environment-aware rendering and interaction in web-based augmented reality. J Imaging2023; 9(3): 63–82.
17.
XinC. Exploring the effectiveness of VR-based product demonstrations featuring items of furniture. Libr Hi Tech2024; 42(2): 471–495.
18.
SunTJinTHuangY, et al.Restoring dunhuang murals: crafting cultural heritage preservation knowledge into immersive virtual reality experience design. Int J Hum Comput Interact2024; 40(8): 2019–2040.
19.
BelhiAAhmedHOAlfaqheriT, et al.An integrated framework for the interaction and 3D visualization of cultural heritage. Multimed Tool Appl2024; 83(15): 46653–46681.
20.
OuYYinPMoL. An improved grey wolf optimizer and its application in robot path planning. Biomimetics2023; 8(1): 84–116.
21.
Nadimi-ShahrakiMHMoeiniETaghianS, et al.Discrete improved grey wolf optimizer for community detection. J Bionic Eng2023; 20(5): 2331–2358.
22.
Rami ReddyMRavi ChandraMLVenkatramanaP, et al.Energy-efficient cluster head selection in wireless sensor networks using an improved grey wolf optimization algorithm. Computers2023; 12(2): 35–51.
23.
LiYLiuLZhaoX, et al.Deep learning-assisted characterization of nanoparticle growth processes: unveiling SAXS structure evolution. Radiat Detect Technol Methods2024; 8(4): 1712–1728.
24.
JeongHParkJEKimN, et al.Deep learning-based detection and quantification of brain metastases on black-blood imaging can provide treatment suggestions: a clinical cohort study. Eur Radiol2024; 34(3): 2062–2071.
25.
PanneerselvamSThangavelSKPonnamVS, et al.Federated learning based fire detection method using local MobileNet. Sci Rep2024; 14(1): 1–21.
26.
ChenCYuGBaoH, et al.A novel multimodal remote-sensing image registration algorithm using phase symmetry and rank-based local self-similarity. Remote Sens Lett2024; 15(12): 1270–1281.
27.
HeZSuJChenL, et al.I recall the past': exploring how people collaborate with generative AI to create cultural heritage narratives. Proc ACM Hum Comput Interact2025; 9(2): 1–30.
28.
QinYChiXShengB, et al.GuideRender: large-scale scene navigation based on multi-modal view frustum movement prediction. Vis Comput2023; 39(8): 3597–3607.
29.
AdhanomIBMacNeilagePFolmerE. Eye tracking in virtual reality: a broad review of applications and challenges. Virtual Real2022; 27(2): 1481–1505.
30.
LiJPengXLiuX, et al.A study on shaping tourists’ conservational intentions towards cultural heritage in the digital era: exploring the effects of authenticity, cultural experience, and place attachment. J Asian Architect Build Eng2025; 24(3): 1965–1984.
31.
BanfiFOreniD. Unlocking the interactive potential of digital models with game engines and visual programming for inclusive VR and web-based museums. Virtual Archaeol Rev2025; 16(32): 44–70.
32.
HasanvandMNooshyarMMoharamkhaniE, et al.Machine learning methodology for identifying vehicles using image processing. Artif Intell Appl2023; 1(3): 170–178.
33.
BhosleKMusandeV. Evaluation of deep learning CNN model for recognition of Devanagari digit. Artif Intell Appl2023; 1(2): 114–118.
