Towards Reducing Severe Defocus Spread Effects for Multi-Focus Image Fusion via an Optimization Based Strategy

• URL: http://arxiv.org/abs/2012.14678v1
• Date: Tue, 29 Dec 2020 09:26:41 GMT
• Title: Towards Reducing Severe Defocus Spread Effects for Multi-Focus Image Fusion via an Optimization Based Strategy
• Authors: Shuang Xu and Lizhen Ji and Zhe Wang and Pengfei Li and Kai Sun and Chunxia Zhang and Jiangshe Zhang
• Abstract summary: Multi-focus image fusion (MFF) is a popular technique to generate an all-in-focus image. This paper presents an optimization-based approach to reduce defocus spread effects. Experiments conducted on the real-world dataset verify superiority of the proposed model.
• Score: 22.29205225281694
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-focus image fusion (MFF) is a popular technique to generate an all-in-focus image, where all objects in the scene are sharp. However, existing methods pay little attention to defocus spread effects of the real-world multi-focus images. Consequently, most of the methods perform badly in the areas near focus map boundaries. According to the idea that each local region in the fused image should be similar to the sharpest one among source images, this paper presents an optimization-based approach to reduce defocus spread effects. Firstly, a new MFF assessmentmetric is presented by combining the principle of structure similarity and detected focus maps. Then, MFF problem is cast into maximizing this metric. The optimization is solved by gradient ascent. Experiments conducted on the real-world dataset verify superiority of the proposed model. The codes are available at https://github.com/xsxjtu/MFF-SSIM.

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