Abstract
Sparse-representation based approaches have been integrated into image fusion methods in the past few years and show great performance in image fusion. Training an informative and compact dictionary is a key step for a sparsity-based image fusion method. However, it is difficult to balance “informative” and “compact”. In order to obtain sufficient information for sparse representation in dictionary construction, this paper classifies image patches from source images into different groups based on morphological similarities. Stochastic coordinate coding (SCC) is used to extract corresponding image-patch information for dictionary construction. According to the constructed dictionary, image patches of source images are converted to sparse coefficients by the simultaneous orthogonal matching pursuit (SOMP) algorithm. At last, the sparse coefficients are fused by the Max-L1 fusion rule and inverted to a fused image. The comparison experimentations are simulated to evaluate the fused image in image features, information, structure similarity, and visual perception. The results confirm the feasibility and effectiveness of the proposed image fusion solution.
Highlights
High-quality images can help increase the accuracy and efficiency of image processing and related analysis
This paper proposes a novel sparse-representation based image fusion framework, which integrates geometric dictionary construction
A geometric image patch classification approach is presented to cluster image patches from different source images based on the similarity of image geometric structure
Summary
High-quality images can help increase the accuracy and efficiency of image processing and related analysis. Zhu et al [9,37,38] presented an image patch clustering method and applied it to corresponding sub-dictionary training process Their method improved the detailed features in medical image fusion. Since a sparse-representation based fusion method uses image blocks for sparse coding and coefficient 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 and sharp line information for dictionary learning, which can improve the accuracy of sparse representation. A geometric-information based classification method is proposed and applied to a sub-dictionary learning of image patches. The proposed classification method can accurately split source image patches into different groups for sub-dictionary learning based on the corresponding geometry features. The remaining sections of this paper are structured as follows: Section 2 proposes the geometric sub-dictionary learning method and integrated image fusion framework; Section 3 compares and analyzes experimentation results; and Section 4 concludes this paper
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