Generation and Recombination for Multifocus Image Fusion With Free Number of Inputs
Huafeng Li, Dan Wang, Yuxin Huang, Yafei Zhang, Zhengtao Yu
Abstract
Multifocus image fusion is an effective method to overcome the limitations of optical lenses. The fused results can be obtained from some existing methods by generating decision maps. However, such methods assume that the focused areas of the two source images are complementary, making it impossible to achieve the simultaneous fusion of multiple images. Additionally, existing methods ignore the impact of hard pixels on the fusion performance, limiting the visual quality improvement of fusion images. To address these issues, a combined generation and recombination model called GRFusion is proposed. In GRFusion, the focus property detection of each source image can be independently implemented, enabling the simultaneous fusion of multiple source images and avoiding information loss caused by alternating fusion. It renders the GRFusion free from the limitation of the number of input images. Furthermore, GRFusion investigates the detection of hard pixels with ambiguous focus properties by analyzing the inconsistencies among the detection results of the focus areas in the source images. This allows the hard pixels to be distinguished from the source images. Besides, a multidirectional gradient embedding method is proposed for generating full-focus images. Subsequently, a hard-pixel-guided recombination mechanism for constructing the fused result is devised to integrate the complementary advantages of feature reconstruction-based and focused pixel recombination-based methods. Extensive experimental results demonstrate the effectiveness and superiority of the proposed method. The source code of the proposed method is available at: https://github.com/lhf12278/GRFusion.