Yongsong Huang

Image Super-Resolution (SR) is a fundamental task in computer vision and image processing. The challenge of super-resolving infrared (IR) or thermal images represents a key and persistent research frontier in the deep learning era. Different from visible-light SR, IRSR is challenged by the distinct properties of thermal images, which typically exhibit low contrast, limited high-frequency details, and sensor-specific noise. This survey provides a comprehensive review of Infrared Image Super-Resolution (IRSR), covering its applications, the inherent limitations of IR imaging hardware, and a detailed taxonomy of processing methodologies. In addition, we discuss the critical roles of datasets and evaluation metrics in the field. Finally, we highlight current technological gaps and identify promising future directions for the community. To keep pace with the rapid developments, we maintain an actively updated repository of relevant work at https://github.com/yongsongH/Infrared Image\_SR\_Survey.

Infrared image super-resolution (IRSR) is challenging due to weak structures and textures. While Mamba-based state-space models (SSMs) efficiently model long-range dependencies, their inherent block-wise processing disrupts spatial consistency, limiting direct IRSR applicability. We propose IRSRMamba, a novel Mamba-based framework overcoming this limitation via tailored structural and textural preservation. Integrated into a Mamba backbone, our key innovations are: 1) Wavelet Transform Feature Modulation (WTFM), enhancing multi-scale frequency-aware feature extraction to mitigate block-induced coherence loss; and 2) an SSMs-based Semantic Consistency Loss, enforcing cross-block alignment to restore fragmented context. IRSRMamba achieves superior global-local fusion, structural coherence, and fine-detail preservation. Experiments show state-of-the-art PSNR, SSIM, and perceptual quality on IR benchmarks, as well as robust generalization to remote sensing. This work establishes Mamba-based architectures as highly promising for high-fidelity IR image enhancement. Code is available at https://github.com/yongsongH/IRSRMamba.

The ability of generative models to accurately fit data distributions has resulted in their widespread adoption and success in fields such as computer vision and natural language processing. In this chapter, we provide a brief overview of the application of generative models in the domain of infrared (IR) image super-resolution, including a discussion of the various challenges and adversarial training methods employed. We propose potential areas for further investigation and advancement in the application of generative models for IR image super-resolution.