VR-enhanced Visual System for Stereoscopic Presentation Display of Oil Paintings

Abstract

Abstract The clarity and fidelity of stereoscopic presentation play a crucial role in the display of oil painting images. However, most existing stereoscopic image presentation methods suffer from poor image quality and low fidelity. To address this issue, this paper raises an image optimization method based on Dark Channel Prior-based Image Dehazing and t-distribution-Stochastic Neighbor Embedding, as well as an oil painting image extraction method based on super-resolution reconstruction and Generative Adversarial Network. These two methods are integrated with the Stable Diffusion model to construct a virtual reality-enhanced visual system for oil painting images. Experimental results show that the system achieves effective optimization for 89% of the image content during the enhancement process. In the process of converting oil paintings into two-dimensional data images, the fidelity reaches 92.5%. The average resolution of the stereoscopic virtual reality images generated by the system reaches 1400 dpi, and the average structural similarity with the original paintings is 0.91. These results demonstrate that the proposed system provides strong clarity and detail fidelity when generating stereoscopic virtual reality images of oil paintings. It effectively addresses the challenge of image quality control in the generation process and offers a new approach and perspective for the stereoscopic presentation of oil paintings, further enhancing the realism of virtual reality visual effects.