Fuzzy Control Method for Synchronous Acquisition of High Resolution Image based on Machine Learning

Aiming at the problem of low baud rate of traditional high-resolution image synchronous acquisition fuzzy control method, a high-resolution image synchronous acquisition fuzzy control method based on machine learning is designed. By detecting the fuzzy edge information of high-resolution image, the fuzzy membership function of synchronous acquisition quantity is proposed, and the gradient amplitude of synchronous acquisition quantity of high-resolution image is calculated. The unsupervised learning algorithm based on machine learning is used to cluster the fuzzy control data, so as to determine the fuzzy space of synchronous acquisition quantity of high-resolution image, and calculate the fuzzy feature similarity, the fuzzy control of synchronous acquisition quantity of high resolution image is realized. Experimental results show that the controlled wave rate in this paper solves the problem of low wave rate in 255.63 bps/h-271.33 bps/h, and significantly improves the control accuracy.

Regularized Multiframe Super-Resolution Image Reconstruction Using Linear and Nonlinear Filters

The primary goal of the multiframe super-resolution image reconstruction is to produce an image with a higher resolution by integrating information extracted from a set of corresponding images with low resolution, which is used in various fields. However, super-resolution image reconstruction approaches are typically affected by annoying restorative artifacts, including blurring, noise, and staircasing effect. Accordingly, it is always difficult to balance between smoothness and edge preservation. In this paper, we intend to enhance the efficiency of multiframe super-resolution image reconstruction in order to optimize both analysis and human interpretation processes by improving the pictorial information and enhancing the automatic machine perception. As a result, we propose new approaches that firstly rely on estimating the initial high-resolution image through preprocessing of the reference low-resolution image based on median, mean, Lucy-Richardson, and Wiener filters. This preprocessing stage is used to overcome the degradation present in the reference low-resolution image, which is a suitable kernel for producing the initial high-resolution image to be used in the reconstruction phase of the final image. Then, L2 norm is employed for the data-fidelity term to minimize the residual among the predicted high-resolution image and the observed low-resolution images. Finally, bilateral total variation prior model is utilized to restrict the minimization function to a stable state of the generated HR image. The experimental results of the synthetic data indicate that the proposed approaches have enhanced efficiency visually and quantitatively compared to other existing approaches.

Artificial intelligence in functional imaging of the lung

Artificial intelligence (AI) is transforming the way we perform advanced imaging. From high-resolution image reconstruction to predicting functional response from clinically acquired data, AI is promising to revolutionize clinical evaluation of lung performance, pushing the boundary in pulmonary functional imaging for patients suffering from respiratory conditions. In this review, we overview the current developments and expound on some of the encouraging new frontiers. We focus on the recent advances in machine learning and deep learning that enable reconstructing images, quantitating, and predicting functional responses of the lung. Finally, we shed light on the potential opportunities and challenges ahead in adopting AI for functional lung imaging in clinical settings.