Abstract
The traditional image to enlarge algorithms include nearest neighbor interpolation, bilinear interpolation and high-order interpolation. In order to achieve super-resolution reconstruction of images, a new algorithm combining traditional algorithms and deep learning is proposed. The framework is divided into two parts. Firstly, the deep reconstruction of the low-resolution data is performed by the ability of deep learning to extract features automatically. Then, combining with the traditional interpolation reconstruction results, the deep learning algorithm is used again for training and learning, and finally the high-resolution reconstructed data is obtained. The algorithm is validated using an online public test data set. The results show that the algorithm has a significant effect on the MSE (mean squared error) and PSNR (Peak Signal to Noise Ratio). Compared with the traditional interpolation algorithm and the single deep learning algorithm, the proposed algorithm has higher performance. Moreover, the proposed algorithm is perfect for the reconstruction of the details, the outline is clear, and the high-quality image is obtained.
Highlights
Super resolution reconstruction is to restore high resolution images from low resolution images or image sequences
In order to solve these problems, we propose to combine the traditional interpolation algorithm with the deep learning theory, and use the simple three-layer Convolutional Neural Network (CNN) combining with the interpolation algorithm to complete the super-resolution reconstruction, and achieve an ideal effect
Combining with the idea of deep learning, we propose a super-resolution reconstruction structure combining the interpolation algorithm and FCN (Fully Convolutional Neural Network)
Summary
Super resolution reconstruction is to restore high resolution images from low resolution images or image sequences. High resolution image means that the image has more detailed information and more exquisite picture quality, which has important application value in the fields of high-definition television, medical image, remote sensing satellite image, etc. In the process of digital image acquisition, due to the influence of many factors such as atmospheric disturbance and defocusing, the quality of the collected images will decline to different degrees. Due to the limitations of environment and hardware, the quality of the collected images is not as good as expected. This led to the idea of improving the resolution of images by improving the hardware, software or
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