Department of Foundational Disciplines, Shijiazhuang People's Medical College, Shijiazhuang, Hebei, China.
Comput Intell Neurosci. 2022 Jun 28;2022:9907786. doi: 10.1155/2022/9907786. eCollection 2022.
To study a high-efficiency online classroom intelligent management system, this article builds an artificial intelligence classroom management system based on the tensor CS reconstruction model. Moreover, this study uses the cosine function to represent the data energy fitting of the traditional active contour model and proposes a local cosine fitting energy active contour model based on partial image restoration, which is used for image and composite image segmentation. Simultaneously, this study proposes a new type of super-resolution algorithm. This algorithm performs Fourier transform of a low-resolution image into a frequency range and then performs an inverse Fourier transform on the image expanded in the frequency range to obtain the initial high-resolution image and finally reconstructs a new super-resolution image using the frequency-domain compressed data of the high-resolution image. Finally, this study verifies and analyzes the performance of the model through experiments. The research results are basically consistent with the expectations of the model.
为了研究高效的在线课堂智能管理系统，本文构建了一个基于张量 CS 重构模型的人工智能课堂管理系统。此外，本研究使用余弦函数来表示传统主动轮廓模型的数据能量拟合，并提出了一种基于局部余弦拟合能量的主动轮廓模型，用于图像和复合图像分割。同时，本研究提出了一种新型的超分辨率算法。该算法对低分辨率图像进行傅里叶变换到频域，然后对频域中扩展的图像进行傅里叶逆变换，得到初始高分辨率图像，最后使用高分辨率图像的频域压缩数据重建新的超分辨率图像。最后，通过实验验证和分析模型的性能。研究结果与模型的预期基本一致。
