A novel region‐based multimodal image fusion technique using improved dictionary learning. Issue 3 (31st December 2019)

• Record Type: Journal Article
• Authors: Meher, Bikash; Agrawal, Sanjay; Panda, Rutuparna; Dora, Lingraj; Abraham, Ajith
• Is Part Of: International journal of imaging systems and technology. Volume 30:Issue 3(2020)
• Page Start: 558
• Page End: 576
• Publication Date: 2019-12-31
• Subjects: dictionary learning -- image fusion -- region‐based fusion -- sparse representation; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22395
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: Recently, the sparse representation (SR) based algorithms have gained much attention from the researchers in the area of image fusion (IF). The building of a compact discriminative dictionary plays a vital role in the sparse‐based IF techniques. In this context, an efficient multimodal IF method based on improved dictionary learning is investigated. The key contributions of this paper are: (a) An improved KSVD algorithm is suggested for the dictionary learning process, (b) to reduce the computational time, only the informative patches are selected using energy feature, and (c) a novel region‐based fusion scheme is suggested for the first time for the problem on hand. The suggested technique is tested with a number of multimodal images from Harvard Medical School brain database. The results are compared with state‐of‐the‐art multiscale transform‐based methods and modified SR‐based methods. Unlike earlier methods, our proposed technique generates an adaptive dictionary through selection of informative patches only. This results in a compact dictionary with improved computational efficiency. The experimental results reveal that our approach outperforms other methods. The potential application of the suggested method could be in pathological images for follow‐up study and better treatment planning.