Single Image Super Resolution Reconstruction Algorithm Based on Deep Learning

Abstract

Abstract Super resolution image reconstruction technology is the core technology in image processing, which provides technical support for most of the intelligent devices for target tracking and target detection. However, the existing image acquisition stage will cause extra high cost to improve the image resolution. Optimization algorithm is one of the best ways to solve this kind of problem. At present, there is no substantial breakthrough in this field in China. Therefore, this paper studies single image super-resolution reconstruction algorithm based on deep learning. In this paper, the application of super-resolution reconstruction algorithm in single image is discussed. The analysis shows that the application of deep learning in this field is shallow and has a large optimization space. Especially for the existing main problems, the traditional algorithm is optimized and improved. According to the demand of resolution reconstruction, combined with the latest deep learning method, the accuracy and robustness of the algorithm are further improved. At the same time, it effectively improves the comprehensive performance of the model. In order to further verify the actual performance of the proposed algorithm, the corresponding comparative experiments are established. The experimental results show that the proposed algorithm has obvious advantages over the traditional fsrcnn algorithm, especially when the PSNR of dataset 4 is 2, 3, and 4 times, it can increase 0.41 dB, 0.58 dB and 0.52 DB respectively. Analysis shows that this algorithm has obvious advantages and achieves ideal results.