Spatial Display Model of Oil Painting Art Based on Digital Vision Design

Oil painting, owing to its unique expressive approach, holds infinite charm in classical artistic creation, yet introduces complexities in terms of manual maintenance. In pursuit of digital spatial visualization of oil painting art, this study employs a stereo matching algorithm and Efficient large-scale stereo matching, focusing on aspects like disparity maps and pixel contrasts. Furthermore, enhancements in the algorithm involve the incorporation of the cross-arms strategy for image registration and the selection of auxiliary point sets to optimize the handling of image features. Results indicate that the proposed model, evaluated on the Middlebury dataset, achieves high accuracy, recall rates, and F1 scores, measuring 97.2%, 95.0%, and 97.5% respectively, surpassing the DecStereo algorithm by 3.4%, 8.2%, and 5.7%. When tested on the Photo2monet oil painting dataset, the proposed model achieves peak signal-to-noise ratio and average structural similarity index values of 16.781 and 0.833 respectively. This suggests that the proposed model excels in digital visual representation of oil paintings, exhibiting higher image precision, stronger stereo matching capabilities, and superior spatial display performance.