Fusion algorithm based on grayscale-gradient estimation for infrared images with multiple integration times

In high dynamic range (HDR) scenes containing strong local radiation, infrared images acquired with a single integration time cannot preserve the details of both bright and dark regions due to the limited dynamic range of the detector. Fusing multiple infrared images captured with variable integration times is an effective method for extending the dynamic range of infrared imaging systems. Fusion algorithms are critical to the visual quality of the results of this technique. In this paper, we propose a fusion algorithm based on grayscale-gradient estimation for infrared images with multiple integration times. In our algorithm, an objective grayscale image and an objective gradient map are first estimated, then they are substituted into the optimization framework for image fusion, and finally, a fused image with appropriate grayscale and gradient distribution is obtained by solving a minimization problem. Experiments show that the proposed algorithm works well under both normal and HDR infrared scenarios. Compared with existing typical multiple exposure fusion algorithms, the proposed algorithm produces better results in terms of noise suppression, visual information fidelity and perceptual quality. Therefore, the proposed algorithm has potential in thermal vision applications involving high dynamic range scenarios and has a high reference value for research in HDR thermal imaging technology.