Ancient murals face significant degradation due to environmental and human factors. Traditional restoration is labor-intensive and inconsistent, while recent advancements in deep learning, particularly diffusion models, offer promising solutions. Diffusion models are known for their strong performance in generative tasks and are particularly well-suited for image restoration by iteratively refining degraded areas. These models can simulate the gradual addition and removal of noise, mimicking the natural restoration process, thereby enabling more precise reconstruction of damaged regions. Leveraging these advantages, this paper proposes a novel mural image restoration model architecture based on diffusion, aimed at addressing the specific challenges of restoring ancient murals. The proposed model combines a heterogeneous UNet structure with two key modules: pixel space augmentation block (PSAB) for enhancing spatial details and dual channel attention block (DCAB) for refining channel information. Experimental results show the model’s effectiveness in restoring large-scale damage and fine textures, outperforming existing methods in PSNR, SSIM, and LPIPS metrics.

Most existing intelligent editing tools for music and video rely on the cross-modal matching technology of the affective consistency or the similarity of feature representations. However, these methods are not fully applicable to complex audiovisual matching scenarios, resulting in low matching accuracy and suboptimal audience perceptual effects due to ambiguous matching rules and associated factors. To address these limitations, this paper focuses on both the similarity and integration of affective distribution for the artistic audiovisual works of movie and television video and music. Based on the rich emotional perception elements, we propose a hybrid matching model based on feature canonical correlation analysis (CCA) and fine-grained affective similarity. The model refines KCCA fusion features by analyzing both matched and unmatched music–video pairs. Subsequently, the model employs XGBoost to predict relevance and to compute similarity by considering fine-grained affective semantic distance as well as affective factor distance. Ultimately, the matching prediction values are obtained through weight allocation. Experimental results on a self-built dataset demonstrate that the proposed affective matching model balances feature parameters and affective semantic cognitions, yielding relatively high prediction accuracy and better subjective experience of audiovisual association. This paper is crucial for exploring the affective association mechanisms of audiovisual objects from a sensory perspective and improving related intelligent tools, thereby offering a novel technical approach to retrieval and matching in music–video editing.

To get the closed boundary of target object, this paper proposed an improved algorithm with disparity-guided constraint for active contour extraction based on greedy snake model. The disparity relationship between the control point and the centre of the object could introduce a new external force, which adds the function of shrinking and expanding for the traditional greedy snake when the curve falls onto texture-less regions. In addition, our method also uses the improved model iteratively with processed control points as the original input. Experiment results have proved this method will obtain the accurate contour made up of uniform and dense points, which is more effective than the traditional greedy snake algorithm.

Audience sentiment analysis is a popular research task in Music Information Retrieval (MIR). In this work, we aim to lean the prediction model of fine-grained music emotion for intelligent editing and retrieval. To satisfy the properties of time-series of music, the RNN-based model of BILSTM was selected as the bone. In particular, the traditional features and deep features extracted from the CNN architecture of spectrogram was fused with attention mechanism to enhance the recognition ability for different emotions. The ablation experiment has proved the necessary of the multi-dimensional features. Finally, the contrasting experiments on recent algorithms demonstrate that our proposed method for emotion prediction is effective, which is also suitable for small data sets.

The purpose of the research on emotion classification of film and television (TV) scene images is to hope that the computer can simulate the audience's emotional perception to judge positive or negative emotional trends of the image. In this paper, from the perspective of emotional semantics, a method of the emotion classification about the film and TV scene image is proposed. Firstly, the film and TV scene image dataset were established and assessed based on the subjective evaluation experiment. Then the theory of psychology and photographic art was used to extract image emotional features. Finally, particle swarm optimization (PSO)-support vector machine (SVM) algorithm based on feature importance dimension reduction was used for building the classifier. The experimental results show that the image dataset initially established in this paper can be helpful to the subsequent study on emotion classification of the film and TV scene image, and the accuracy of the positive and negative emotion classifier on this dataset is 0.87, which conforms to audience's emotional perception of the film and TV scene image.

This article investigates the impact of visual color perception on fine-grained emotion prediction in videos, analyzing the contribution of color perception features in fine-grained emotion prediction. A total of 20 subjects were involved in this experiment. First, 10 subjects conducted a fine-grained emotional subjective evaluation experiment on 50 video clips. Then, another 10 subjects conducted a subjective evaluation experiment of color perception annotation for these 50 video clips. On this basis, the correlation and mechanism between color perceptual features and fine-grained emotions were analyzed. Finally, a fine-grained emotion prediction model was established based on the combination of objective features and color perceptual features. It was also observed that, compared to using only objective features, incorporating perceptual features enhanced the model’s accuracy. This article also compared the importance of different perceptual features for each emotion and explained the mechanism between color perception and fine-grained emotions. By selecting the top 24 important features to predict emotions, the association between perceptual features and emotions was more effectively captured.