Generation and Recombination for Multifocus Image Fusion with Free Number of Inputs

TL;DR

The paper introduces GRFusion, a novel multifocus image fusion method that independently detects focus regions and adaptively combines multiple images, improving fusion quality and flexibility regardless of input number.

Contribution

It proposes a flexible, input-independent fusion model with a focus detection mechanism and a hard pixel-guided recombination strategy, enhancing fusion performance.

Findings

Outperforms existing methods in fusion quality.

Effectively handles multiple input images.

Demonstrates robustness across various scenarios.

Abstract

Multifocus image fusion is an effective way to overcome the limitation of optical lenses. Many existing methods obtain fused results by generating decision maps. However, such methods often assume that the focused areas of the two source images are complementary, making it impossible to achieve simultaneous fusion of multiple images. Additionally, the existing methods ignore the impact of hard pixels on fusion performance, limiting the visual quality improvement of fusion image. To address these issues, a combining generation and recombination model, termed as GRFusion, is proposed. In GRFusion, focus property detection of each source image can be implemented independently, enabling simultaneous fusion of multiple source images and avoiding information loss caused by alternating fusion. This makes GRFusion free from the number of inputs. To distinguish the hard pixels from the source images, we achieve the determination of hard pixels by considering the inconsistency among the detection results of focus areas in source images. Furthermore, a multi-directional gradient embedding method for generating full focus images is proposed. Subsequently, a hard-pixel-guided recombination mechanism for constructing fused result is devised, effectively integrating the complementary advantages of feature reconstruction-based method and focused pixel recombination-based method. Extensive experimental results demonstrate the effectiveness and the superiority of the proposed method.The source code will be released on https://github.com/xxx/xxx.