# Inf-OSRGAN: Optimized Blind Super-Resolution GAN for Infrared Images
## 1 Introduction  
With the rapid development of computer vision and digital image processing, super-resolution technology has garnered significant interest and served as an important image enhancement method. Such technology aims to reconstructing high-resolution images from their low-resolution counterparts, improving the quality and clarity of the images. It holds significant importance across diverse areas of image processing, such as surveillance video enhancement, remote monitoring, and medical image analysis [1; 2; 3; 4; 5]. On the other hand, infrared images, as a special type of image, are extensively utilized and valued in military, security, and medical fields. Their unique physical properties and information content offer enhanced thermal and concealed insights, critical for applications such as target recognition, night vision, and thermal imaging. Owing to their unique physical properties and information sources, infrared images offer a wealth of concealed and thermal data, thereby playing a pivotal role in areas such as target recognition, night vision, and thermal imaging [6; 7; 8; 9]. However, low-resolution infrared images lack detail, which limits their effectiveness in practical applications. Therefore, developing effective methods to enhance the resolution of infrared images is imperative for recovering lost details and improving their utility in critical applications. Compared to visible light images, infrared images usually have fewer textures and less detail. This poses a challenge in accurately reconstructing the missing details ininfrared super-resolution tasks. Currently, most super-resolution datasets [10; 11; 12; 13] focus on the visible light domain. The degradation processes of infrared images are more varied than, and are distinct from, those affecting visible light images. Classical degradation models for visible light images, which include blur, downsampling, noise, and JPEG compression [14; 15], are relatively straightforward and fail to accurately simulate the complex degradation processes of infrared images. Practical degradation processes [16; 17] are more complex but relatively singular, often overlooking the potential for simpler degradation scenarios. This implies that direct application of these models to infrared images may not adequately address degradation issues, thereby impacting super-resolution performance. Infrared images typically exhibit less structural clarity compared to visible light images, because of increased blurriness and discontinuity. Consequently, models must accurately capture and reconstruct the structural information in infrared images. Deep-learning-based image super-resolution networks encompass convolutional neural networks, deep residual networks, and generative adversarial networks. The commonly employed loss functions in the super-resolution domain, including pixel-level loss [18; 19; 20] and perceptual loss [21; 22], fail to differentiate between structural and appearance attributes. Furthermore, applying RGB three-channel super-resolution models, originally designed for visible light images, to infrared image processing can lead to substantial redundant computation, and such redundant computations do not yield additional useful information pertinent to infrared images. To address these issues, this paper proposes a deep-learning-based super-resolution method for infrared images. In order to effectively tackle the limitations and challenges associated with infrared image processing, several key strategies were adopted. First, we constructed a large-scale dataset dedicated to infrared image super-resolution research, which contains scene-rich high-resolution infrared image samples. This dataset provided ample data support and served as a solid foundation for our study. It enabled us to train and evaluate our method on diverse and realistic datasets.