Year-end Sale % OFF 
Abstract
Multi-focus image fusion is an effective approach to obtain the all-in-focus image. Focus detection is the key issue of multi-focus image fusion. Aiming at the shortcoming of spatial domain and transform domain algorithms for multi-focus image fusion, a novel multi-focus image fusion algorithm is proposed by combing focus detection in spatial domain and non-subsampled contourlet transform (NSCT) domain. At first, the focused pixels are detected by the sum-modified-Laplacian algorithm in spatial domain. At the same time, the focus detection method is proposed in NSCT domain, namely by MPCNN and voting fusion methods for high-frequency subbands of NSCT. Then, the morphological operation is utilized to correct the focus detection results in spatial domain and NSCT domain. At last, synthesis of detection results is implemented and the fused image can be obtained. Experimental results verified that the proposed algorithm outperformed some state-of-the-art fusion algorithms in terms of both subjective observation and objective evaluations.
Highlights
Accepted: August 13, 2018Published: September 20, 2018
The focus detection result (FlagT) in nonsubsampled contourlet transform (NSCT) domain is acquired by MPCNN and voting strategy for high- frequency subbands
(4) The artificial database which is produced by adding Gaussian blur to part of the original images with different standard derivations and Gaussian filter with different size
Summary
Data Availability Statement: All relevant data are within the paper and its Supporting Information files. Especially the high-frequency subbands, can describe the salient features more effectively, transform domain-based fusion algorithms suffer from blurring effect because the results are usually obtained by image reconstruction which modifies the original image information to a certain extent. Inspired by these properties, a novel image fusion scheme by combining spatial information and transformation information is proposed in this paper. The directional number of each level is 21, 22 and 22, respectively
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
