模糊医学计算机视觉图像恢复与可视化应用。
Fuzzy Medical Computer Vision Image Restoration and Visual Application.
机构信息
School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009, China.
School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China.
出版信息
Comput Math Methods Med. 2022 Jun 21;2022:6454550. doi: 10.1155/2022/6454550. eCollection 2022.
In order to shorten the image registration time and improve the imaging quality, this paper proposes a fuzzy medical computer vision image information recovery algorithm based on the fuzzy sparse representation algorithm. Firstly, by constructing a computer vision image acquisition model, the visual feature quantity of the fuzzy medical computer vision image is extracted, and the feature registration design of the fuzzy medical computer vision image is carried out by using the 3D visual reconstruction technology. Then, by establishing a multidimensional histogram structure model, the wavelet multidimensional scale feature detection method is used to achieve grayscale feature extraction of fuzzy medical computer vision images. Finally, the fuzzy sparse representation algorithm is used to automatically optimize the fuzzy medical computer vision images. The experimental results show that the proposed method has a short image information registration time, less than 10 ms, and has a high peak PSNR. When the number of pixels is 700, its peak PSNR can reach 83.5 dB, which is suitable for computer image restoration.
为了缩短图像配准时间，提高成像质量，本文提出了一种基于模糊稀疏表示算法的模糊医学计算机视觉图像信息恢复算法。首先，通过构建计算机视觉图像采集模型，提取模糊医学计算机视觉图像的视觉特征量，并利用三维视觉重建技术对模糊医学计算机视觉图像进行特征配准设计。然后，通过建立多维直方图结构模型，采用小波多维尺度特征检测方法实现模糊医学计算机视觉图像的灰度特征提取。最后，采用模糊稀疏表示算法对模糊医学计算机视觉图像进行自动优化。实验结果表明，该方法的图像信息注册时间短，小于 10ms，峰值 PSNR 高。当像素数为 700 时，其峰值 PSNR 可达 83.5dB，适用于计算机图像恢复。
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