Significance   Infrared image dehazing refers to the process of restoring the contrast and visual quality of the infrared imaging system by removing the influence of haze, smoke and other media on the infrared image in the presence of atmospheric turbulence. Infrared images are widely used in military, security, medical, energy exploration and other fields by virtue of the advantages of all-day and no light limitation. Enhanced Image Visibility : Infrared images captured in hazy or foggy conditions often suffer from reduced visibility and degraded image quality. Dehazing techniques aim at improving the visibility of these images, allowing for better interpretation and analysis. Improved Object Detection and Recognition: Dehazing infrared images can enhance the performance of object detection and recognition algorithms. By removing the haze, important visual features of objects can be more clearly revealed, leading to more accurate and reliable results in various applications such as surveillance, target tracking, and autonomous vehicles. Enhanced Environmental Monitoring: Infrared imaging is widely used in environmental monitoring, including forest fire detection, air pollution monitoring, and thermal inspection of infrastructure. Dehazing techniques can help improve the accuracy and reliability of these monitoring systems by providing clearer and more detailed infrared images. Enhanced Human Perception: Dehazing infrared images can also benefit human observers by providing clearer and more understandable visual information. This is particularly important in applications where human operators rely on infrared images for decision-making, such as search and rescue operations, firefighting, and security surveillance. Advancements in Computer Vision Research: Dehazing infrared images presents a challenging problem in computer vision research. Developing effective dehazing algorithms for infrared images requires the exploration and development of novel techniques, such as image enhancement, deconvolution, and scene understanding. The research in this area can contribute to the advancement of overall computer vision research and benefit other related fields.

  Progress   In recent years, with the continuous development of computer vision and deep learning technologies, significant progress has been made in infrared image dehazing techniques, providing support for the development of infrared image applications. According to the different types of data relied upon in the process of infrared image dehazing, existing methods can be divided into two categories: multi-information fusion and single-frame image processing. Image dehazing is a highly challenging task because the degradation level of an image is influenced by factors such as the concentration of suspended particles and the distance between the target and the detector. These pieces of information are difficult to directly obtain from the image, making image dehazing a very challenging task. Researchers have proposed multi-information fusion algorithms to assist in the restoration of infrared images by fusing additional information acquired through sensor fusion or multiple images. These methods mainly include polarization image dehazing (Fig.2) and fusion-weighted image dehazing methods. Single-frame image processing refers to the technique of digital or image processing applied to individual static images. In practical applications, single-frame image processing is often combined with machine learning, deep learning, and other technologies to achieve better results. This article mainly discusses image enhancement and image reconstruction in single-frame image processing. Image enhancement combines the MSR (Fig.5) with the CLAHE algorithm to achieve image enhancement of foggy images (Fig.3, Fig.4). Image reconstruction applied to the field of infrared image dehazing can estimate unknown information based on the characteristics of known information, which can be used to restore the degraded image quality caused by haze conditions. The main methods include: Dark Channel Prior, Super pixel and MRF (Fig.7), Atmospheric Light Estimation-based (Fig.8), Color Attenuation Prior-based (Fig.9), Detail Transmission Prior-based Image, Gradient Channel Prior-based Dehazing Algorithm. Overall, both multi-modal fusion and single-frame image processing approaches contribute to the advancement of infrared image dehazing techniques by leveraging different types of data and image processing algorithms.

  Conclusions and Prospects   Infrared image dehazing technology will become more intelligent. Researchers are more inclined to use deep learning and convolutional neural network (CNN) techniques to achieve automated haze removal processing. In the future, infrared image dehazing technology is expected to be deeply integrated with other image processing techniques. Multi-modal fusion is a technique used to extract the most useful information from multiple data sources in order to improve the understanding and processing of image data, to enhance image quality and processing efficiency. To improve the accuracy of infrared image dehazing, it can be beneficial to incorporate visible light images or depth images.