Abstract
In recent years, sparse representation approaches have been integrated into multi-focus image fusion methods. The fused images of sparse-representation-based image fusion methods show great performance. Constructing an informative dictionary is a key step for sparsity-based image fusion method. In order to ensure sufficient number of useful bases for sparse representation in the process of informative dictionary construction, image patches from all source images are classified into different groups based on geometric similarities. The key information of each image-patch group is extracted by principle component analysis (PCA) to build dictionary. According to the constructed dictionary, image patches are converted to sparse coefficients by simultaneous orthogonal matching pursuit (SOMP) algorithm for representing the source multi-focus images. At last the sparse coefficients are fused by Max-L1 fusion rule and inverted to fused image. Due to the limitation of microscope, the fluorescence image cannot be fully focused. The proposed multi-focus image fusion solution is applied to fluorescence imaging area for generating all-in-focus images. The comparison experimentation results confirm the feasibility and effectiveness of the proposed multi-focus image fusion solution.
Highlights
Following the development of cloud computing, cloud environment provides more and more strong computation capacity to process various images [1,2,3]
Based on the classification of image patches, this paper proposed sparse representation-based approach that uses principle component analysis (PCA) algorithm to construct more informative and compact dictionary [38]
This paper proposes a novel sparse representation-based image fusion framework, which integrates geometric dictionary construction
Summary
Following the development of cloud computing, cloud environment provides more and more strong computation capacity to process various images [1,2,3]. Kim and Han [38] proposed a joint clustering dictionary learning method for image fusion They used the steering kernel regression to strength the local geometric features of source images first. A geometric classification based dictionary learning method is proposed for sparse-representation based image fusion. Since sparse-representation based fusion method uses image blocks for sparse coding and coefficients fusion, extracting underlying geometric information from image-block groups is an efficient way to construct a dictionary. The geometric classification can group image blocks based on edge, sharp line information for dictionary learning, which can improve the accuracy of sparse representation. The rest sections of this paper are structured as follows: Section 2 proposes the geometric dictionary learning method and multi-focus image fusion framework; Section 3 compares and analyzes experimentation results; and Section 4 concludes this paper
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