Morphology and Active Contour Model for Multi-Focus Image Fusion

Bai, Xiangzhi; Liu, Miaoming; Chen, Zhiguo; Wang, Peng

doi:10.1109/dicta.2015.7371286

Cited by 3 publications

(2 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The feature space transform based fusion has emerged as another popular mean for multi-focus image fusion [ 32 ]. These fusion methods estimate the focus map through a single scale feature space of source images.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

“…Sheng et al [29] proposed a support value transform based method that uses a support vector machine to achieve the fused image. The discrete cosine transform has also been exploited for image fusion, e.g., [30,31] The feature space transform based fusion has emerged as another popular mean for multi-focus image fusion [32]. These fusion methods estimate the focus map through a single scale feature space of source images.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Exploiting Superpixels for Multi-Focus Image Fusion

Ilyas

Farid

Khan

et al. 2021

Entropy

View full text Add to dashboard Cite

Multi-focus image fusion is the process of combining focused regions of two or more images to obtain a single all-in-focus image. It is an important research area because a fused image is of high quality and contains more details than the source images. This makes it useful for numerous applications in image enhancement, remote sensing, object recognition, medical imaging, etc. This paper presents a novel multi-focus image fusion algorithm that proposes to group the local connected pixels with similar colors and patterns, usually referred to as superpixels, and use them to separate the focused and de-focused regions of an image. We note that these superpixels are more expressive than individual pixels, and they carry more distinctive statistical properties when compared with other superpixels. The statistical properties of superpixels are analyzed to categorize the pixels as focused or de-focused and to estimate a focus map. A spatial consistency constraint is ensured on the initial focus map to obtain a refined map, which is used in the fusion rule to obtain a single all-in-focus image. Qualitative and quantitative evaluations are performed to assess the performance of the proposed method on a benchmark multi-focus image fusion dataset. The results show that our method produces better quality fused images than existing image fusion techniques.

show abstract

Section: Background and Literature Reviewmentioning

confidence: 99%

Section: Background and Literature Reviewmentioning

confidence: 99%