Depth-Distilled Multi-Focus Image Fusion

Homogeneous regions, which are smooth areas that lack blur clues to discriminate if they are focused or non-focused. Therefore, they bring a great challenge to achieve high accurate multi-focus image fusion (MFIF). Fortunately, we observe that depth maps are highly related to focus and defocus, containing a preponderance of discriminative power to locate homogeneous regions. This offers the potential to provide additional depth cues to assist MFIF task. Taking depth cues into consideration, in this paper, we propose a new depth-distilled multi-focus image fusion framework, namely D2MFIF. In D2MFIF, depth-distilled model (DDM) is designed for adaptively transferring the depth knowledge into MFIF task, gradually improving MFIF performance. Moreover, multi-level fusion mechanism is designed to integrate multi-level decision maps from intermediate outputs for improving the final prediction. Visually and quantitatively experimental results demonstrate the superiority of our method over several state-of-the-art methods.

Automated summary

A new depth-distilled multi-focus image fusion framework (D2MFIF) is proposed in this paper, which gradually improves the performance of multi-focus image fusion by adaptively transferring depth knowledge and integrating multi-level decision maps.