Application of image super-resolution recognition and artificial intelligence system in repairing students' psychological education problems.

Infrared thermography has become a mature and widely accepted technology with applications in many different fields, from medical to industrial [...]

Infrared imaging and NDT

Ultrasound imaging is widely used as a noninvasive lesion detection method in diagnostic medicine. Improving the quality of these ultrasound images is very important for accurate diagnosis, and deep learning-based algorithms have gained significant attention. This study proposes a multiscale deep encoder–decoder with phase congruency (MSDEPC) algorithm based on deep learning to improve the quality of diagnostic ultrasound images. The MSDEPC algorithm included low-resolution (LR) images and edges as inputs and constructed a multiscale convolution and deconvolution network. Simulations were conducted using the Field 2 program, and data from real experimental research were obtained using five clinical datasets containing images of the carotid artery, liver hemangiomas, breast malignancy, thyroid carcinomas, and obstetric nuchal translucency. LR images, bicubic interpolation, and super-resolution convolutional neural networks (SRCNNs) were modeled as comparison groups. Through visual assessment, the image processed using the MSDEPC was the clearest, and the lesions were clearly distinguished. The structural similarity index metric (SSIM) value of the simulated ultrasound image using the MSDEPC algorithm improved by approximately 38.84% compared to LR. In addition, the peak signal-to-noise ratio (PSNR) and SSIM values of clinical ultrasound images using the MSDEPC algorithm improved by approximately 2.33 times and 88.58%, respectively, compared to LR. In conclusion, the MSDEPC algorithm is expected to significantly improve the spatial resolution of ultrasound images.

A Multiscale Deep Encoder–Decoder with Phase Congruency Algorithm Based on Deep Learning for Improving Diagnostic Ultrasound Image Quality

Infrared Image Edge Recognition Algorithm Based on Partial Differential Equation for Industry 4.0

With the rapid development of digital technology, it has now been widely used for the digital protection of cultural heritage. The traditional cultural heritage digital protection method of information retrieval recall rate is low, and the processing time is long. Therefore, a new digital protection method of cultural heritage based on web technology is proposed. An improved four-layer architecture design pattern is adopted to plan a web-based digital protection platform for cultural heritage. The application service provider (ASP) combines SQL server and B/S architecture to develop a database platform and functional modules for direct dynamic management of the website model. Based on this, combined with web technology, the digital image of cultural heritage is reconstructed followed by computing the uncertainty probability of web visualization, completing the modeling metalanguage, and classification of network cultural heritage. The digital image enhancement of cultural heritage is based on HSV color space. In order to realize the digital protection of traditional cultural heritage, multifiltering technology is introduced into the process of brightness digital image extraction. It is evident from the experimental results that the proposed method has higher recall and lower error and can enhance the digital image of cultural heritage in a shorter time, which shows that the proposed method can effectively achieve digital protection of cultural heritage.

https://downloads.hindawi.com/journals/mpe/2022/3196063.pdf

Digital Protection of Cultural Heritage Based on Web Technology

In this paper, an adaptive dual-regularization super-resolution reconstruction algorithm based on sub-pixel convolution (MPSR) is proposed. There are two novel features of the algorithm: First, the traditional regularization algorithm and sub-pixel convolution algorithm are combined to enrich the details; then, a regularization function with two adaptive parameters and two regularization terms is proposed to enhance the edge. MPSR firstly enhances the multi-scale detail of low-resolution images; then, regular processing and feature extraction are carried out; finally, sub-pixel convolution is used to fuse the extracted features to generate high-resolution images. The experimental results show that the subjective and objective evaluation indexes (PSNR/SSIM) of the algorithm have achieved satisfactory results.

/pdf/super-resolution-reconstruction-algorithm-for-infrared-image-5ebclnc1bz.pdf

Super-Resolution Reconstruction Algorithm for Infrared Image with Double Regular Items Based on Sub-Pixel Convolution

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Super-Resolution Reconstruction Algorithm for Infrared Image with Double Regular Items Based on Sub-Pixel Convolution