China boasts a rich and diverse array of both tangible and intangible cultural heritage. Today, the use of digital technologies for the modeling, preservation, and presentation of cultural heritage has become a research hotspot in the fields of cultural heritage conservation, as well as in related areas such as computer graphics and computer vision. The Cultural Heritage Digitization National-Local Joint Engineering Research Center at Northwest University conducts research in five key areas: 3D modeling equipment for cultural relics from the three dynasties, smart museum construction, virtual restoration of ceramic artifacts, the restoration of ancient human faces, and the intelligent media fusion for holographic performances of Qin Opera. However, due to the fundamental differences between tangible and intangible cultural heritage, many challenges arise in areas such as modeling methods, restoration and preservation technologies, and presentation forms. These specific challenges include: ① Existing digital modeling equipment for cultural relics is inefficient and requires significant human intervention. ② The wide variety of cultural relics, with their complex features, diverse shapes, and rich semantics, necessitates the development of knowledge extraction and knowledge graph construction methods tailored to Chinese cultural relics for efficient organization and presentation. ③ Research on the shape representation, description methods, and automatic recombination of damaged relic fragments. ④ Virtual restoration of ancient human faces, including gender and racial recognition. ⑤ Holographic performance technology faces challenges such as high computational power demands, the accuracy of the fusion of art and technology, hardware compatibility, real-time processing, immersion, and interactivity, while also needing to address cultural differences and audience acceptance issues. In response to these needs and technical challenges, this paper first reviews the relevant literature from recent years, then summarizes a series of achievements by the Cultural Heritage Digitization National-Local Joint Engineering Research Center at Northwest University, and finally discusses future research directions in the field of cultural heritage digitization.

Qin Opera, as a treasure of Chinese traditional theatre art, has a profound historical heritage. However, the early video materials of Qin Opera are often affected by noise and distortion, resulting in poor picture quality, which seriously hampers the preservation quality of Qin Opera digital archives. Currently applied video denoising techniques often do not make full use of the temporal coherence of the video frame sequence when dealing with the colorful and complex texture of Qin Opera’s costumes, which makes the denoising effect unsatisfactory and makes it difficult to effectively retain the core features of the video frames. In this paper, we carry out research on the Qin Opera video denoising algorithm based on the attention mechanism, and the main research contents are as follows: Aiming at the existing video denoising algorithms ignoring the temporal correlation between frames which leads to the problem of poor effect, we propose a new video denoising algorithm, which makes use of the double gating attention mechanism for the fusion of the temporal sequence information. The algorithm firstly integrates the timing information of consecutive video frames effectively through the timing fusion module; then accurately identifies and eliminates the timing noise using the dual-gated attention denoising network; finally, the features are further refined through the multi-head interactive attention refining module to eliminate the artifacts that may be generated during the denoising process and recover the lost details, to enhance the quality of the denoised image. The experimental results demonstrate that compared with existing methods such as DVDNet, ViDeNN, and FastDVDNet, this method can make better use of the timing information of the video to achieve clean and efficient denoising of Qin Opera videos.

The segmentation of microscopic bubbles of porcelain relics can provide a clearer observation of the morphology, quantity, and distribution of micro bubbles on the surface of porcelain, which is of great significance in assisting relic experts in classifying porcelain cultural relic fragments and identifying porcelain cultural relics. However, the bubbles in porcelain microscopic images are complex and varied, with uneven size and distribution. Existing image segmentation methods are difficult to adapt to the characteristics of porcelain microscopic bubbles.Therefore, a network named AGUNet++ based on convolution attention unit is proposed. This network utilizes a zigzag connection approach between nodes to fully extract image semantic features and prevent information loss. Meanwhile, a convolution attention unit is introduced by combining the dense skip connection of the convolution unit with the attention gate. The CAU enhances the learning of bubble regions relevant to the task of microscopic bubble segmentation in porcelain artifacts while suppressing irrelevant regions. Deep supervision and cross entropy loss are applied to the output of each sub network layer during the training process, which effectively enhance the ability to extract microscopic bubble features in porcelain artifacts and refine the segmentation results. The experimental results of this method on the SD-saliency-900 and PRMI demonstrate that AGUNet++exhibits certain improvements in MIoU, Precision, Recall, and F1_score, showing better segmentation performance compared to classical image segmentation networks.