Affiliation:
1. Department of Electronics and Communication Engineering, Jaypee University of Information Technology, Solan, Himachal Pradesh, India
Abstract
Background:
Empirical curvelet and ridgelet image fusion is an emerging technique in the field of image processing that aims to combine the benefits of both
transforms.
Objective:
Computerized Tomography (CT) and magnetic Resonance Imaging (MR) brain images.
Methods:
An empirical coefficient selection strategy is then employed to identify the most significant coefficients from both domains based on their
magnitude and directionality. These selected coefficients are coalesced using a fusion rule to generate a fused coefficient map. To reconstruct the
image, an inverse curvelet and ridgelet transform was applied to the fused coefficient map, resulting in a high-resolution fused image that
incorporates the salient features from both input images.
Results:
The experimental outcomes on real-world datasets show how the suggested strategy preserves crucial information, improves image quality, and
outperforms more conventional fusion techniques. For CT Ridgelet-MR Curvelet and CT Curvelet-MR Ridgelet, the authors' maximum PSNRs
were 58.97 dB and 55.03 dB, respectively. Other datasets are compared with the suggested methodology.
Conclusion:
The proposed method's ability to capture fine details, handle complex geometries, and provide an improved trade-off between spatial and spectral
information makes it a valuable tool for image fusion tasks.
Publisher
Bentham Science Publishers Ltd.