This work presents a structure-preserving non-photorealistic rendering (NPR) framework that can produce an effective structure-preserving abstracted and stylized output by manipulating visual features from 2D color image. The proposed framework distills the prominent structural features, dominant edges, medium-scale details, curved discontinued edges, silhouette, dendritic structures and curved boundaries and suppresses the superfluous details like noise, texture, irregular gradients, small-scale details and block artifacts. This framework effectively enhanced the significant image properties such as color, contrast, edge strength and sharpness at every stage based on the obtained statistical features availability information and the predefined conditions. This leads to enhancement of quality assessment features such as PSNR, SSIM and suppressing the image complexity and noise. It considers image and object space information to produce abstraction and stylization, thereby identifying emphasized elements of the structure using Harris feature detector algorithm. The proposed framework effectively preserves the structural features in the foreground of an image by comprehensively integrating the sequence of NPR image filters through rigorous experimental analysis simultaneously diminishing the background content of an image. Implementation of the proposed work is carried out in MATLAB 2018 with high-performance computer of 6.6 teraflop/s computing environment and Nvidia Tesla P100 GPU. The proposed framework evaluates every stage output with various subjective matters and quality assessment techniques with various statistical essences. By this manner, contextual features in an image have been identified and well preserved. Effectiveness of the proposed work has been validated by conducting the experiments taking David Mould dataset and Flickr images as references and comparing the obtained results with similar contemporary work cited in the literature. In addition, user’s visual feedback and the standard quality assessment techniques were also used to evaluate the work. Finally, this work lists out the structures preserving applications, constraints, framework implementation challenges and future work in the fields of image abstraction and stylization.

这项工作提出了一种保留结构的非真实感渲染（NPR）框架，该框架可以通过操纵2D彩色图像的视觉特征来产生有效的保留结构的抽象化和风格化输出。拟议的框架提炼了突出的结构特征，优势边缘，中等尺度的细节，弯曲的不连续边缘，轮廓，树状结构和弯曲的边界，并抑制了多余的细节，例如噪声，纹理，不规则的渐变，小尺度的细节和块状假象。该框架基于获得的统计特征可用性信息和预定义条件，有效增强了每个阶段的重要图像属性，例如颜色，对比度，边缘强度和清晰度。这样可以增强质量评估功能，例如PSNR，SSIM和抑制图像复杂性和噪点。它考虑图像和对象空间信息以产生抽象和样式化，从而使用哈里斯特征检测器算法识别结构的强调元素。通过严格的实验分析，同时减少图像的背景内容，通过全面整合NPR图像滤镜的序列，该框架有效地保留了图像前景中的结构特征。拟议工作的实现是在MATLAB 2018中使用6.6 teraflop / s计算环境的高性能计算机和Nvidia Tesla P100 GPU进行的。提议的框架使用各种主观事项和具有各种统计本质的质量评估技术来评估每个阶段的输出。通过这种方式 图像中的上下文特征已被识别并保存完好。通过以David Mold数据集和Flickr图像为参考进行实验，并将获得的结果与文献中引用的类似当代作品进行比较，验证了所提出工作的有效性。此外，还使用了用户的视觉反馈和标准的质量评估技术来评估工作。最后，这项工作列出了保留应用程序，约束，框架实现挑战以及图像抽象和样式化领域中的未来工作的结构。通过以David Mold数据集和Flickr图像为参考进行实验，并将获得的结果与文献中引用的类似当代作品进行比较，验证了所提出工作的有效性。此外，还使用了用户的视觉反馈和标准的质量评估技术来评估工作。最后，这项工作列出了保留应用程序，约束，框架实现挑战以及图像抽象和样式化领域中的未来工作的结构。通过以David Mold数据集和Flickr图像为参考进行实验，并将获得的结果与文献中引用的类似当代作品进行比较，验证了所提出工作的有效性。此外，还使用了用户的视觉反馈和标准的质量评估技术来评估工作。最后，这项工作列出了保留应用程序，约束，框架实现挑战以及图像抽象和样式化领域中的未来工作的结构。