Multi-focus Image Fusion Using Content Adaptive Blurring
M.S. Farid, A. Mahmood, S.A. Al-Maadeed, "Multi-focus Image Fusion Using Content Adaptive Blurring," Information Fusion, Jan. 2018
Abstract: Multi-focus image fusion has emerged as an important research area in information fusion. It aims at increasing the depth-of-field by extracting focused regions from multiple partially focused images, and merging them together to produce a composite image in which all objects are in focus. In this paper, a novel multi-focus image fusion algorithm is presented in which the task of detecting the focused regions is achieved using a Content Adaptive Blurring (CAB) algorithm. The proposed algorithm induces non-uniform blur in a multi-focus image depending on its underlying content. In particular, it analyzes the local image quality in a neighborhood and determines if the blur should be induced or not without losing image quality. In CAB, pixels belonging to the blur regions receive little or no blur at all, whereas the focused regions receive significant blur. Absolute difference of the original image and the CAB-blurred image yields initial segmentation map, which is further refined using morphological operators and graph-cut techniques to improve the segmentation accuracy. Quantitative and qualitative evaluations and comparisons with current state-of-the-art on two publicly available datasets demonstrate the strength of the proposed algorithm.
Datasets used in Performance Evaluation
- Lytro Dataset
This dataset is proposed in M. Nejati, S. Samavi, and S. Shirani, "Multi-focus Image Fusion Using Dictionary-Based Sparse Representation", Information Fusion, vol. 25, Sept. 2015, pp. 72-84
- Grayscale Dataset
This dataset is a collection of famous multi-focus image pairs which are widely used in liturature for performance evaluation of image fusion algorithms.
Results achieved by the Proposed Algorithm
- Results on Lytro Dataset (coming soon)
- Results on Grayscale Dataset (coming soon)
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Last updated: January 10, 2018