Abstract
The convolutional neural network has achieved good results in the superresolution reconstruction of single-frame images. However, due to the shortcomings of infrared images such as lack of details, poor contrast, and blurred edges, superresolution reconstruction of infrared images that preserves the edge structure and better visual quality is still challenging. Aiming at the problems of low resolution and unclear edges of infrared images, this work proposes a two-stage generative adversarial network model to reconstruct realistic superresolution images from four times downsampled infrared images. In the first stage of the generative adversarial network, it focuses on recovering the overall contour information of the image to obtain clear image edges; the second stage of the generative adversarial network focuses on recovering the detailed feature information of the image and has a stronger ability to express details. The infrared image superresolution reconstruction method proposed in this work has highly realistic visual effects and good objective quality evaluation results.
Highlights
Infrared imaging technology used for passive noncontact detection and identification has the advantages of good concealment, strong transmission ability, no interference from electromagnetic waves, and good low-light and night-vision capabilities [1]
Chao et al [5] proposed the use of a convolutional neural network (CNN) to achieve the superresolution reconstruction of visible light images and to learn the mapping relationship between lowresolution image and high-resolution image by training with a large dataset
To generate a high-resolution image with photographgrade realistic details and inspired by the literature [22, 23], we propose a simple and effective two-layer generative adversarial network (GAN), in which the image generation process is divided into two stages (Figure 2)
Summary
Infrared imaging technology used for passive noncontact detection and identification has the advantages of good concealment, strong transmission ability, no interference from electromagnetic waves, and good low-light and night-vision capabilities [1]. Superresolution reconstruction refers to the reconstruction of a high-resolution image or a sequence on single-frame or multiframe low-resolution images and includes three main types, i.e., interpolation-based methods, reconstruction-based methods, and instance-based learning methods. Instance-based learning methods are flexible in algorithm structure, can provide more details under high magnification, and have become a research hotspot of superresolution reconstruction in recent years. Chao et al [5] proposed the use of a convolutional neural network (CNN) to achieve the superresolution reconstruction of visible light images and to learn the mapping relationship between lowresolution image and high-resolution image by training with a large dataset. The proposal of a generative adversarial network (GAN) [9] meets the demands of generative models for research and application in many fields. GAN uses unsupervised learning methods, enabling the production of more clear and true
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