A Novel Multi-Focus Image Fusion Method Based on Stochastic Coordinate Coding and Local Density Peaks Clustering

Abstract

The multi-focus image fusion method is used in image processing to generate all-focus images that have large depth of field (DOF) based on original multi-focus images. Different approaches have been used in the spatial and transform domain to fuse multi-focus images. As one of the most popular image processing methods, dictionary-learning-based spare representation achieves great performance in multi-focus image fusion. Most of the existing dictionary-learning-based multi-focus image fusion methods directly use the whole source images for dictionary learning. However, it incurs a high error rate and high computation cost in dictionary learning process by using the whole source images. This paper proposes a novel stochastic coordinate coding-based image fusion framework integrated with local density peaks. The proposed multi-focus image fusion method consists of three steps. First, source images are split into small image patches, then the split image patches are classified into a few groups by local density peaks clustering. Next, the grouped image patches are used for sub-dictionary learning by stochastic coordinate coding. The trained sub-dictionaries are combined into a dictionary for sparse representation. Finally, the simultaneous orthogonal matching pursuit (SOMP) algorithm is used to carry out sparse representation. After the three steps, the obtained sparse coefficients are fused following the max L1-norm rule. The fused coefficients are inversely transformed to an image by using the learned dictionary. The results and analyses of comparison experiments demonstrate that fused images of the proposed method have higher qualities than existing state-of-the-art methods.

A Novel Geometric Dictionary Construction Approach for Sparse Representation Based Image Fusion

“…Fusion results showed that the detailed information from source images was perfectly reserved. 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.…”

“…Geometric information, as one type of the most important image information, including edges, contours, and textures of image, can remarkably influence the quality of image perception [39,40]. This information can be used in patch classification as a supervised dictionary prior to improving the performance of the trained dictionary [41,42].…”

An Integrated Dictionary-Learning Entropy-Based Medical Image Fusion Framework

“…One classical approach of dictionary learning is the alternate minimization scheme optimizes dictionary learning process [41,48]. It alternates two minimization steps: one with respect to dictionary D with the fixed sparse code A, and one with respect to A with fixed D. Although it is not as fast as a well-tuned stochastic gradient descent algorithm in practice, it is parameter-free.…”

A Geometric Dictionary Learning Based Approach for Fluorescence Spectroscopy Image Fusion

“…The resized vectors are sparse coded with trained dictionary to sparse coefficients z i 1 , z i 2 , ..., z i n . In the second step, the sparse coefficients are fused by 'Max-L1' fusion rule [50][51][52]. Then the fused coefficients are inverted to fused image by the trained dictionary.…”