Abstract Infrared and visible image fusion (IVIF) is an essential branch of image fusion, and enhancing the visible image of IVIF can significantly improve the fusion performance. However, many existing low‐light enhancement methods are unsuitable for the visible image enhancement of IVIF. In order to solve this problem, this paper proposes a new visible image enhancement method for IVIF. Firstly, the colour balance and contrast enhancement‐based self‐calibrated illumination estimation (CCSCE) is proposed to improve the input image's brightness, contrast, and colour information. Then, the method based on Mutually Guided Image Filtering (muGIF) is adopted to design a strategy to extract details adaptively from the original visible image, which can keep details without introducing additional noise effectively. Finally, the proposed visible image enhancement technique is used for IVIF tasks. In addition, the proposed method can be used for the visible image enhancement of IVIF and other low‐light images. Experiment results on different public datasets and IVIF demonstrate the authors’ method's superiority from both qualitative and quantitative comparisons. The authors’ code will be publicly available at https://github.com/yiqiao666/low‐light‐enhancement‐for‐IVIF/tree/master .

By integrating effective features of multi-modality medical images to provide richer information, multi-modality medical image fusion has been substantially used in computer-aided diagnosis applications. However, many existing fusion schemes do not consider how to eliminate the effects of the noise in source medical images and cannot provide enough details and textures for disease diagnosis. To address the problems above, we propose a new fidelity-driven optimization (FDO) reconstruction and details preserving guided-based fusion method for multi-modality medical images. To overcome the influence of noise in multi-modality medical images, a rank coefficient optimization method of low-rank approximation based on weighted mean curvature is proposed to reconstruct multi-modality medical image. Moreover, we propose an iterative detail preserving guided fusion (DPGF) method to integrate more textures and detail information of source multi-modality medical images, while ensuring high signal-to noise ratios. The experimental results show that the proposed method outperforms some of the state-of-the-art fusion methods. Specifically, the extensive experiments prove that our method has high robustness for noisy medical images, which also indicates the application prospects in diagnosis applications.