Infrared Image Super-Resolution Enhancement Based on Convolutional Neural Network
Jintao Wang, Yingna Liu · 2019 International Joint Conference on Information, Media and Engineering (IJCIME) · 2019
With the rapid development of modern scientific research, the research of infrared technology has achieved a lot of results, and infrared detectors and other devices have also been widely used. However, infrared images have characteristics such as low contrast, unclear information, and blurred edges, which seriously affect the observation effect of infrared images. In order to better achieve observation and monitoring, it is necessary to optimize and enhance the acquired images. In this paper, the infrared image acquired by FLIR-Tau camera is taken as the research object. According to the characteristics of infrared image and the structure of convolutional neural network, an improved super-resolution convolutional neural network algorithm is proposed to train the algorithm model for infrared image. The infrared image is enhanced by the learning characteristics of the convolutional neural network, which is better than the traditional algorithm in contrast enhancement and detail information enhancement.