Super resolution reconstruction method for infrared images based on pseudo transferred features

High-quality infrared images are limited by optical diffraction limits, device material, and technology with a high acquisition cost. Improving image quality with super-resolution reconstruction is an important way to obtain high-resolution infrared images. Based on pseudo texture transfer, an infrared image super-resolution reconstruction algorithm is proposed in this paper. First, an image-to-image transfer learning framework is used to generate pseudo-infrared images from the high-resolution visible image set. Second, the feature maps of pseudo-infrared and low-resolution infrared images are extracted simultaneously, and the feature matching module is designed to calculate the similarity of feature maps with different layers between pseudo-infrared and low-resolution infrared images. The local feature maps with the maximum similarity replace the feature map of low-resolution infrared images. Finally, the original low-resolution image and the corresponding exchanged feature map are used in the proposed feature transfer model to gradually reconstruct the super-resolution image from high level to low level. Experimental results prove that the proposed method has improved performance in terms of quantitative indicators and visual quality.