Search

Graphic design, as a product of the burgeoning new media era, has seen its users' requirements for images continuously evolve. However, external factors such as light and noise often cause graphic design images to become distorted during acquisition. To enhance the definition of these images, this paper introduces a novel image enhancement model based on visual features. Initially, a histogram equalization (HE) algorithm is applied to enhance the graphic design images. Subsequently, image feature extraction is performed using a dual-flow network comprising convolutional neural network (CNN) and Transformer architectures. The CNN employs a residual dense block (RDB) to embed spatial local structure information with varying receptive fields. An improved attention mechanism module, attention feature fusion (AFF), is then introduced to integrate the image features extracted from the dual-flow network. Finally, through image perception quality guided adversarial learning, the model adjusts the initial enhanced image's color and recovers more details. Experimental results demonstrate that the proposed algorithm model achieves enhancement effects exceeding 90% on two large image datasets, which represents a 5%-10% improvement over other models. Furthermore, the algorithm exhibits superior performance in terms of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) image quality evaluation metrics. Our findings indicate that the fusion model significantly enhances image quality, thereby advancing the field of graphic design and showcasing its potential in cultural and creative product design., Competing Interests: The authors declare there are no competing interests., (©2024 Xu et al.)

Scene-based image semantic extraction and its precise sentiment expression significantly enhance artistic design. To address the incongruity between image features and sentiment features caused by non-bilinear pooling, this study introduces a generative adversarial network (GAN) model that integrates visual relationships with sentiment semantics. The GAN-based regularizer is utilized during training to incorporate target information derived from the contextual information into the process. This regularization mechanism imposes stronger penalties for inaccuracies in subject-object type predictions and integrates a sentiment corpus to generate more human-like descriptive statements. The capsule network is employed to reconstruct sentences and predict probabilities in the discriminator. To preserve crucial focal points in feature extraction, the Convolutional Block Attention Mechanism (CBAM) is introduced. Furthermore, two bidirectional long short-term memory (LSTM) modules are used to model both target and relational contexts, thereby refining target labels and inter-target relationships. Experimental results highlight the model's superiority over comparative models in terms of accuracy, BiLingual Evaluation Understudy (BLEU) score, and text preservation rate. The proposed model achieves an accuracy of 95.40% and the highest BLEU score of 16.79, effectively capturing both the label content and the emotional nuances within the image., Competing Interests: The authors declare there are no competing interests., (©2024 Shen and Wang.)

Art designs exhibit different principles, textures, color combinations, and creative skills for vivid thinking visualizations. Art exhibits are far from ages, periods, and creators finding their digital patterns in recent years for resurrection. Degraded periodic artworks are digitally handled for reviving their legacy using digital image processing. This article introduces Textural Restoration Technique (TRT) using Deep Feature Processing (DFP) to augment such innovations. The proposed technique analyses the tampered image for its textures, and available features are extracted. The textures are expected to be sequential based on gradient distribution; the missing gradients are identified from the available features near the region of interest (ROI). The ROI is marked by combining missing and available features from which textural edges are sketched. In this process, recurrent learning is employed for verifying the gradient substitutions for even textures. The texture patterns are classified using high and low accuracy features exhibited between two successive ROIs. First, the learning model is trained using gradient distribution accuracy pursued by the texture completion edge. The second training is pursued by the first distribution, achieving the maximum restoration. The filled features and their gradient positions are marked by moving the ROIs for distinguishing textures. The restoration ratio is computed with high accuracy based on the filled edges., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

The rapid advancement of industrialization has sparked the emergence of diverse art and design theories. As a trailblazer in the realm of industrial art and design theory, visual communication has transcended the boundaries of merely arranging and combining individual elements. Embracing the potential of artificial intelligence technology, the extraction of multidimensional abstract data and the acceleration of the art design process have gained considerable momentum. This study delves into the abstract emotional facets within the methodology of visual communication art design. Initially, convolutional neural networks (CNN) are employed to extract expressive features from the poster's visual information. Subsequently, these features are utilized to cluster emotional elements using a variational autoencoder (VAE). Through this clustering process, the poster images are categorized into positive, negative, and neutral classes. Experimental results demonstrate a silhouette coefficient surpassing 0.7, while the system framework exhibits clustering accuracy and efficiency exceeding 80% in single sentiment class testing. These outcomes underscore the efficacy of the proposed CNN-VAE-based clustering framework in analyzing the dynamic content of design elements. This framework presents a novel approach for future art design within the realm of visual communication., Competing Interests: The author declares that there are no competing interests., (©2023 Rui.)

The purpose of this study was to understand the influence mechanism of college students' entrepreneurial intention in view of the increasing number of college students at present to alleviate college students' employment competition. The psychological factors that influence the entrepreneurial tendency of art graduates were analyzed and studied. First, venture capital and factors affecting entrepreneurial performance were analyzed. Second, the coefficient calculation is carried out for college students majoring in art through the regression analysis of the logistic model. Finally, a team entrepreneurial performance questionnaire was designed, and team reward levels were discussed. The results show that the logistic model can well reflect the real situation of the respondents. The significance level of the entrepreneurial team was 0.02, which was correlated. Additionally, corresponding suggestions were put forward according to the questionnaire results. Clear team goals, assignment of tasks to members, good pressure resistance, and psychological quality of members are necessary qualities for successful entrepreneurship. This conclusion provides a certain theoretical basis for the current college students' entrepreneurial learning and a reliable inspiration for helping college students to successfully start a business., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Li, Wang, Long, Ye and Gao.)