An Improved Super-Resolution Algorithm for Infrared Images Based on Deep Learning

Image super-resolution is widely used and research on its algorithm are also developed rapidly. In recent years, deep learning has been introduced into the process of image super-resolution, and the output image has been improved effectively. On this foundation, this paper proposes to reconstruct the mapping layer with a method that reduces the dimension of extracted features at first and then extends them at last. Also, a deconvolution layer is used at the end of the network to map an uninterpolated low-resolution image to a high-resolution image directly. Besides, smaller convolution kernels and more mapping layers are used in this algorithm. Comparative experiments demonstrate that the above methods can accelerate the speed and increase the effectiveness of the image reconstruction by optimizing the network structure and reducing the computational complexity.