Abstract
A bio-inspired two-scale image complementarity evaluation method is proposed. This novel multi-scale method provides a promising alternative for the performance assessment of image fusion algorithms. Moreover, it can also be used to compare and analyze the multi-scale difference of raw images. Two metrics are presented and used to assess the complementarity of fusion images in non-subsampled contourlet transform (NSCT) domains: visual saliency differences (VSDs) at the coarse scales and detail similarities (DSs) at the fine scales. Visual attention mechanism (VAM)-based saliency maps are combined with NSCT low-pass subbands to compute the VSDs, and linear correlation and contrast consistency-based DSs are compared in NSCT band-pass subbands. Five main multi-scale transform (MST)-based fusion algorithms were compared by using 30 groups of raw images that consist of four types of fusion images. Effects of NSCT filters and decomposition levels on evaluation results are discussed in detail. Furthermore, a group of color multi-exposure fusion images were also taken as examples to evaluate the complementarity of raw images. Experimental results demonstrate the effectiveness of the proposed method, especially for MST-based image fusion algorithms.
Highlights
As image sensing techniques continue to develop, more and more image data from various kinds of image sensors have become available
non-subsampled contourlet transform (NSCT) is employed as the multi-scale transform (MST) tool
We can draw the following conclusions: [1] Five algorithms have approximately equal visual saliency differences (VSDs) compared with two raw images, and RI2 contributes slightly more to fusion results
Summary
As image sensing techniques continue to develop, more and more image data from various kinds of image sensors have become available. Due to the limitations of electronic imaging technologies, such as spectrum range, dynamic range, temporal and spatial resolution, etc., images captured by a single image sensor can only partially This is an Open Access article published by World Scientific Publishing Company. Among hundreds of variations of image fusion techniques, multi-scale transform (MST)-based pixel-level fusion algorithms have become the most important category in recent years.. In an MST-based fusion algorithm, each raw image is first decomposed into a low-frequency subband and a group of high-frequency subbands with different scales and orientations. The capabilities to extract information from different scales can be taken as a quality index for MST-based fusion algorithms. 2. Evaluating the multi-scale visual differences of raw images, and quantitatively selecting the best characteristic distributions of raw images.
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 callMore From: International Journal of Wavelets, Multiresolution and Information Processing
Disclaimer: 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.
