A Geometric Dictionary Learning Based Approach for Fluorescence Spectroscopy Image Fusion

Abstract

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.

Multi-Modality Medical Image Fusion Using Convolutional Neural Network and Contrast Pyramid

“…According to the obtained measurements, the corresponding weights are finally assigned to different sources. To improve the fusion performance, many complicated decomposition methods and detailed weight assignment strategies have been proposed in recent years [28,[40][41][42][43][44][45]. However, it is not easy to design an ideal activity level measurement or weight assignment strategy, which can consider all key issues [37].…”

Optics and Spectroscopy for Fluid Characterization

“…The evaluation of such images, however, is not always straightforward. For this purpose, Zhu et al [16] propose a sparse-representation-based image fusion method. They combine principle component analysis (PCA) to initially extract geometric similarities and classify the images.…”

Fisher Discriminative Sparse Representation Based on DBN for Fault Diagnosis of Complex System

“…Sparse representation is widely used in pattern recognition, image processing, and fault diagnosis [29][30][31][32][33][34][35], since it can include danger rejection as a restriction in the representation model and iterate training data to learn the discriminative dictionaries. Then, the corresponding sparse coefficients can be used to perform fault diagnosis in a complex system.…”