The field of infrared image processing is rapidly advancing, with a focus on developing innovative methods for image fusion and target detection. Recent research has emphasized the importance of integrating global and local information, as well as leveraging multi-scale features and evolutionary learning techniques. Notably, the use of deep learning architectures and wavelet transforms has shown significant potential in improving image quality and detection accuracy. Furthermore, the incorporation of contrastive learning and object-aware approaches has enabled more effective fusion of infrared and visible images. Overall, these advancements are paving the way for enhanced performance in various applications, including surveillance and object detection. Noteworthy papers include: MSCA-Net, which proposes a novel network architecture for infrared small target detection, achieving outstanding performance on several benchmark datasets. DCEvo, which introduces a discriminative cross-dimensional evolutionary learning framework for infrared and visible image fusion, demonstrating significant improvements in visual quality and perception accuracy.