Abstract
Image fusion is significant in various distinct sectors of image processing, from remote sensing to medical applications. In recent years, real-valued wavelet transforms have been utilized to fuse images. This approach has offered enhancements against various poor approaches; however, this task lacks the shift variance and suffers from the directionality connected with its wavelet bases. Moreover, the conventional architecture of this hard wavelet decomposition implements a very hard resolution of filters to attain an essential quarter shift in the coefficient result. The establishment of image fusion methodology is to provide data integrated from distinct images to avoid inconsistency and redundancy presented among the images. This approach is utilized to enhance the utilization, reliability, accuracy, and interpretation of the data with the development of image data transparency by creating an accurate and clear detail of the monitored target. In this research work, a transform-aided image fusion mechanism is utilized to enhance the effectiveness in a better way. With the support of this approach, good “peak signal-to-noise ratio (PSNR)” with a minimum “mean square error (MSE)” can be achieved. Therefore, this work is aimed to implement a new multi-image fusion approach by fusing the normal images. Initially, the standard normal images are manually collected for the approach. Then, the decomposition of two images in the same scene is done through “optimal non-subsampled shearlet transform (ONSST),” where the attributes of NSST are optimized with the help of recommended fitness improved puzzle optimization algorithm (FIPOA). Moreover, the high-frequency fusion is done by optimal weighted average fusion, and low-frequency fusion is carried out by filter mapping-based fusion. In the end, the inverse ONSST is taken to get the final integrated images. The experimental analysis of the recommended approach is evaluated with various performance measures. The validation shows that the developed model attains 44.7%, 6.18%, 17.4%, 17.4%, and 9.7% enhanced performance than DOX-ONSST, AOA-ONSST, SFO-ONSST, and POA-ONSST in terms of standard deviation. The experimental analysis of the developed model shows better performance rather than the existing approaches. The image fusion is widely applicable in the field of clinical and healthcare applications.
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The suggested multimodal image fusion approach utilized the manually collected dataset.
References
Chen, C.I.: Fusion of PET and MR brain images based on IHS and log-Gabor transforms. IEEE Sens. J. 17(21), 6995–7010 (2017)
Xu, X., Wang, Y., Chen, S.: Medical image fusion using discrete fractional wavelet transform. Biomed. Signal Process. Control. 27, 103–111 (2016)
Chai, P., Luo, X., Zhang, Z.: Image fusion using quaternion wavelet transform and multiple features. IEEE Access 5, 6724–6734 (2017)
Lifeng, Y., Donglin, Z., Weidong, W., Shanglian, B.: Multi-modality medical image fusion based on wavelet analysis and quality evaluation. J. Syst. Eng. Electron. 12(1), 42–48 (2016)
Wang, L., Li, B., Tian, L.F.: EGGDD: an explicit dependency model for multi-modal medical image fusion in shift-invariant shearlet transform domain. Inform. Fusion 19, 29–37 (2014)
Yang, Y., Que, Y., Huang, S., Lin, P.: Multimodal sensor medical image fusion based on type-2 Fuzzy logic in NSCT domain. IEEE Sens. J. 16(10), 3735–3745 (2016)
Du, J., Li, W., Xiao, B., Nawaz, Q.: Union Laplacian pyramid with multiple features for medical image fusion. Neurocomputing 194, 326–339 (2016)
Bhatnagar, G., Wu, Q.M.J., Liu, Z.: Human visual system inspired multi-modal medical image fusion framework. Expert Syst. Appl. 40(5), 1708–1720 (2013)
Das, S., Kundu, M.K.: A neuro-fuzzy approach for medical image fusion. IEEE Trans. Biomed. Eng. 60(12), 3347–3353 (2013)
Parisotto, S., Calatroni, L., Bugeau, A., Papadakis, N., Schönlieb, C.-B.: Variational osmosis for non-linear image fusion. IEEE Trans. Image Process. 29, 5507–5516 (2020)
Hou, B., Wei, Q., Zheng, Y., Wang, S.: Unsupervised change detection in SAR image based on gauss-log ratio image fusion and compressed projection. IEEE J. Select. Topics Appl. Earth Observat. Remote Sens. 7(8), 3297–3317 (2014)
Wang, G., Li, W., Gao, X., Xiao, B., Du, J.: Functional and anatomical image fusion based on gradient enhanced decomposition model. IEEE Trans. Instrum. Meas. 71, 1–14 (2022)
Liu, P., Zhang, L., Li, M., Zhang, X.: An efficient algorithm to highlight details in infrared and visible image fusion. IEEE Access 9, 110223–110235 (2021)
Kumar, M., Dass, S.: A total variation-based algorithm for pixel-level image fusion. IEEE Trans. Image Process. 18(9), 2137–2143 (2009)
Zhu, R., Li, X., Zhang, X., Wang, J.: HID: the hybrid image decomposition model for MRI and CT fusion. IEEE J. Biomed. Health Inform. 26(2), 727–739 (2022)
Du, J., Li, W., Tan, H.: Three-layer image representation by an enhanced illumination-based image fusion method. IEEE J. Biomed. Health Inform. 24(4), 1169–1179 (2020)
Zhang, K., Wang, M., Yang, S., Jiao, L.: Convolution structure sparse coding for fusion of panchromatic and multispectral images. IEEE Trans. Geosci. Remote Sens. 57(2), 1117–1130 (2019)
Duan, Z., Zhang, T., Luo, X., Tan, J.: DCKN: multi-focus image fusion via dynamic convolutional kernel network. Signal Process. 189, 108282 (2021)
You, C.-S., Yang, S.-Y.: A simple and effective multi-focus image fusion method based on local standard deviations enhanced by the guided filter. Displays 72, 102146 (2022)
Huang, D., Liu, J., Zhou, S., Tang, W.: Deep unsupervised endoscopic image enhancement based on multi-image fusion. Comput. Methods Progr. Biomed. 221, 106800 (2022)
Boyuan, M., Yu, Z., Xiang, Y., Xiaojuan, B., Haiyou, H., Michele, M.: SESF-Fuse: an unsupervised deep model for multi-focus image fusion. Neural Comput. Appl. 33, 5793–5804 (2021)
Yang, Y., Wu, J., Huang, S., Fang, Y., Lin, P., Que, Y.: Multimodal medical image fusion based on fuzzy discrimination with structural patch decomposition. IEEE J. Biomed. Health Inform. 23(4), 1647–1660 (2019)
Zuo, Q., Zhang, J., Yang, Y.: DMC-fusion: deep multi-cascade fusion with classifier-based feature synthesis for medical multi-modal images. IEEE J. Biomed. Health Inform. 25(9), 3438–3449 (2021)
Duan, J., Mao, S., Jin, J., Zhou, Z., Chen, L., Chen, C.L.P.: A novel GA-based optimized approach for regional multimodal medical image fusion with superpixel segmentation. IEEE Access 9, 96353–96366 (2021)
Daniel, E.: Optimum wavelet-based homomorphic medical image fusion using hybrid genetic-grey wolf optimization algorithm. IEEE Sens. J. 18(16), 6804–6811 (2018)
Gao, G., Xu, L., Feng, D.: Multi-Focus Image Fusion Based on Non-Subsampled Shearlet Transform. Wiley, New Jersey (2013)
Fatemeh, A.Z., Mohammad, D.: POA: puzzle optimization algorithm. Int. J. Intell. Eng. Syst. 15(1), 15826 (2022)
Asha C.S., Shyam L., Varadraj P.G., Prakash Saxena P.U., Multi-modal medical image fusion with adaptive weighted combination of NSST bands using chaotic grey wolf optimization IEEE, (2019)
Liu, S., Wang, J., Lu, Y., Hu, S., Ma, X., Wu, Y.: Multi-focus image fusion based on residual network in non-subsampled shearlet domain. IEEE Access 7, 152043–152063 (2019)
Naidu, V.P.S.: Discrete cosine transform based image fusion techniques. J. Commun. Navig. Signal Process. 1(1), 35–45 (2012)
Yang, Y., Tong, S., Huang, S., Lin, P.: Dual-tree complex wavelet transform and image block residual-based multi-focus image fusion in visual sensor networks. Sensors 14(12), 22408–22430 (2014)
Nagaraja Kumar, N., Jayachandra Prasad, T., Satya Prasad, K.: Multimodal medical image fusion with improved multi-objective meta-heuristic algorithm with fuzzy entropy. J. Inform. Knowl. Manag. 22, 2250063 (2023)
Zuo, Q., Zhang, J., Yang, Y.: DMC-fusion: deep multi-cascade fusion with classifier-based feature synthesis for medical multi-modal images. IEEE J. Biomed. Health Inform. 25(9), 3438–3449 (2021)
Singh, R., Srivastava, R., Prakash, O., Khare, A.: Multimodal medical image fusion in dual-tree complex wavelet transform domain using maximum and average fusion rules. J. Med. Imag. Health Inform. 2, 168–173 (2012)
Bairwa, A.K., Joshi, S., Singh, D.: Dingo optimizer: a nature-inspired metaheuristic approach for engineering problems. Math. Probl. Eng. 9, 1–12 (2021)
Abualigah, L., Diabat, A., Mirjalili, S., Abd Elaziz, M., Gandomi, A.H.: The arithmetic optimization algorithm. Comput. Methods Appl. Mech. Eng. 376, 113609 (2021)
Yuan, Z., Wang, W., Wang, H., Razmjooy, N.: A new technique for optimal estimation of the circuit-based PEMFCs using developed sunflower optimization algorithm. Energy Rep. 6, 662–671 (2020)
Xu, H., Fan, F., Zhang, H., Le, Z., Huang, J.: A deep model for multi-focus image fusion based on gradients and connected regions. IEEE Access 8, 26316 (2020)
Selvakanmani, S., Ashreetha, B.G., Naga, R.D., Shubhrojit, M., Jayavadivel, R., Suresh, B.P.: Deep learning approach to solve image retrieval issues associated with IOT sensors. Measurem. Sens. 24, 100458 (2022)
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Ravi, J., Subbayamma, B.V., Kumar, P.V. et al. Multi-image fusion: optimal decomposition strategy with heuristic-assisted non-subsampled shearlet transform for multimodal image fusion. SIViP 18, 2297–2307 (2024). https://doi.org/10.1007/s11760-023-02906-3
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DOI: https://doi.org/10.1007/s11760-023-02906-3