IEEE Trans Image Process. 2012 Aug;21(8):3390-404. doi: 10.1109/TIP.2012.2197013. Epub 2012 May 1.
Perceptual analysis is an interesting topic in the field of image processing, and can be considered a missing link between image processing and human vision. Of the various forms of perception, one of the most important and best known is shape perception. In this work, a framework based on the online non local patch means (NLPM) method is developed, which is designed to infer possible perceptual observations of an input image using the knowledge images provided. Thanks to the speed of online NLPM, the proposed method can simulate the transformation of the input image to the final perceptual image in real time. In order to improve the performance of the method, a hidden chain series is considered for the model that delivers faster convergence. The capability of the method is evaluated on several well-known perceptual examples.
感知分析是图像处理领域中一个有趣的课题，可以被看作是图像处理和人类视觉之间缺失的一环。在各种感知形式中，最重要和最著名的一种是形状感知。在这项工作中，开发了一个基于在线非局部补丁均值（NLPM）方法的框架，旨在使用提供的知识图像推断输入图像的可能感知观察。由于在线 NLPM 的速度，所提出的方法可以实时模拟输入图像到最终感知图像的转换。为了提高方法的性能，为模型考虑了一个隐藏的链式系列，以实现更快的收敛。该方法的性能在几个著名的感知示例上进行了评估。
