Research on Multichannel 3D Oil Painting Style Rendering Model Based on 3DS Max

Abstract

People are becoming less comfortable in content as a form of data throughout this period of growing scientific and technological advancements. Oil painting also was challenged in unprecedented ways by modern image processing techniques as person’s primary source of data, and it had a tremendous effect on the domain of artistic creativity. Therefore, we present an upgraded convolutional neural network (U-CNN) for the enhancement of style rendering model addressing the aforementioned challenges. Initially, the datasets are gathered that are denoised and enhanced in the preprocessing stage to eliminate noise and improve the quality of the data. Gabor filter bank (GFB) is employed in the feature extraction stage to extract several features from the normalized data. For the application of style rendering model, the proposed approach is utilized. Moreover, through applying the 3DS MAX model, the three-dimensional (3-D) oil painting is generated. Finally, the performance of the proposed approach is examined and compared with other existing approaches to obtain the proposed approach with the greatest effectiveness. The findings are depicted in chart form by employing the origin tool.