Ambikumar, A.S., Bailey, D.G., Gupta, G.S.: Extending the depth of field in microscopy: A review. In: 2016 International Conference on Image and Vision Computing New Zealand (IVCNZ), pp. 1–6 (2016). IEEE

Dowski, E.R., Cathey, W.T.: Extended depth of field through wave-front coding. Applied optics 34(11), 1859–1866 (1995)

Article  PubMed  Google Scholar 

Akpinar, U., Sahin, E., Meem, M., Menon, R., Gotchev, A.: Learning wavefront coding for extended depth of field imaging. IEEE transactions on image processing 30, 3307–3320 (2021)

Article  PubMed  Google Scholar 

Mo, X., Zhang, T., Wang, B., Huang, X., Kuang, C., Liu, X.: Alleviating image artifacts in wavefront coding extended depth of field imaging system. Optics Communications 436, 232–238 (2019)

Article  CAS  Google Scholar 

Cohen, N., Yang, S., Andalman, A., Broxton, M., Grosenick, L., Deisseroth, K., Horowitz, M., Levoy, M.: Enhancing the performance of the light field microscope using wavefront coding. Optics express 22(20), 24817–24839 (2014)

Article  PubMed  PubMed Central  Google Scholar 

Hermessi, H., Mourali, O., Zagrouba, E.: Multimodal medical image fusion review: Theoretical background and recent advances. Signal Processing 183, 108036 (2021)

Article  Google Scholar 

Huang, B., Yang, F., Yin, M., Mo, X., Zhong, C.: A review of multimodal medical image fusion techniques. Computational and mathematical methods in medicine 2020 (2020)

Pertuz, S., Puig, D., Garcia, M.A.: Analysis of focus measure operators for shape-from-focus. Pattern Recognition 46(5), 1415–1432 (2013)

Article  Google Scholar 

Pan, C., Chen, J., Zhang, R., Zhuang, S.: Extension ratio of depth of field by wavefront coding method. Optics express 16(17), 13364–13371 (2008)

Article  PubMed  Google Scholar 

Zhao, T., Mauger, T., Li, G.: Optimization of wavefront-coded infinity-corrected microscope systems with extended depth of field. Biomedical optics express 4(8), 1464–1471 (2013)

Article  PubMed  PubMed Central  Google Scholar 

Gierlak, M., Albrecht, S., Kauer, J., Leverenz, E., Beckers, I.E.: Wavefront coding using a spatial light modulator for extended depth of field microscopy. In: European Conference on Biomedical Optics, p. 879803 (2013). Optica Publishing Group

Cao, Z., Zhai, C., Li, J., Xian, F., Pei, S.: Combination of color coding and wavefront coding for extended depth of field. Optics Communications 392, 252–257 (2017)

Article  CAS  Google Scholar 

Elmalem, S., Giryes, R., Marom, E.: Learned phase coded aperture for the benefit of depth of field extension. Optics express 26(12), 15316–15331 (2018)

Article  PubMed  Google Scholar 

Du, H., Dong, L., Liu, M., Zhao, Y., Wu, Y., Li, X., Jia, W., Liu, X., Hui, M., Kong, L.: Increasing aperture and depth of field simultaneously with wavefront coding technology. Applied Optics 58(17), 4746–4752 (2019)

Article  PubMed  Google Scholar 

Wei, X., Han, J., Xie, S., Yang, B., Wan, X., Zhang, W.: Experimental analysis of a wavefront coding system with a phase plate in different surfaces. Applied Optics 58(33), 9195–9200 (2019)

Article  PubMed  Google Scholar 

Li, Y., Wang, J., Zhang, X., Hu, K., Ye, L., Gao, M., Cao, Y., Xu, M.: Extended depth-of-field infrared imaging with deeply learned wavefront coding. Optics Express 30(22), 40018–40031 (2022)

Article  PubMed  Google Scholar 

Valdecasas, A.G., Marshall, D., Becerra, J.M., Terrero, J.: On the extended depth of focus algorithms for bright field microscopy. Micron 32(6), 559–569 (2001)

Article  CAS  PubMed  Google Scholar 

Aguet, F., Van De Ville, D., Unser, M.: Model-based 2.5-d deconvolution for extended depth of field in brightfield microscopy. IEEE Transactions on Image Processing 17(7), 1144–1153 (2008)

Forster, B., Van De Ville, D., Berent, J., Sage, D., Unser, M.: Complex wavelets for extended depth-of-field: A new method for the fusion of multichannel microscopy images. Microscopy research and technique 65(1-2), 33–42 (2004)

Article  PubMed  Google Scholar 

Tessens, L., Ledda, A., Pizurica, A., Philips, W.: Extending the depth of field in microscopy through curvelet-based frequency-adaptive image fusion. In: 2007 IEEE International Conference on Acoustics, Speech and Signal Processing-ICASSP’07, vol. 1, p. 861 (2007). IEEE

Dogan, H., Baykal, E., Ekinci, M., Ercin, M.E., Ersoz, S.: A novel extended depth of field process based on nonsubsampled shearlet transform by estimating optimal range in microscopic systems. Optics Communications 429, 88–99 (2018)

Article  CAS  Google Scholar 

Costa, M.G.F., Pinto, K., Fujimoto, L.B., Ogusku, M.M., Costa Filho, C.F.: Multi-focus image fusion for bacilli images in conventional sputum smear microscopy for tuberculosis. Biomedical Signal Processing and Control 49, 289–297 (2019)

Piccinini, F., Tesei, A., Zoli, W., Bevilacqua, A.: Extended depth of focus in optical microscopy: Assessment of existing methods and a new proposal. Microscopy research and technique 75(11), 1582–1592 (2012)

Article  PubMed  Google Scholar 

Chen, J., Li, X., Luo, L., Mei, X., Ma, J.: Infrared and visible image fusion based on target-enhanced multiscale transform decomposition. Information Sciences 508, 64–78 (2020)

Article  Google Scholar 

Li, L., Si, Y., Wang, L., Jia, Z., Ma, H.: A novel approach for multi-focus image fusion based on sf-papcnn and isml in nsst domain. Multimedia Tools and Applications 79, 24303–24328 (2020)

Article  Google Scholar 

Ramlal, S.D., Sachdeva, J., Ahuja, C.K., Khandelwal, N.: An improved multimodal medical image fusion scheme based on hybrid combination of nonsubsampled contourlet transform and stationary wavelet transform. International Journal of Imaging Systems and Technology 29(2), 146–160 (2019)

Article  Google Scholar 

Tan, W., Tiwari, P., Pandey, H.M., Moreira, C., Jaiswal, A.K.: Multimodal medical image fusion algorithm in the era of big data. Neural Computing and Applications, 1–21 (2020)

Hermessi, H., Mourali, O., Zagrouba, E.: Multimodal medical image fusion review: Theoretical background and recent advances. Signal Processing 183, 108036 (2021)

Article  Google Scholar 

Liu, Y., Wang, L., Cheng, J., Li, C., Chen, X.: Multi-focus image fusion: A survey of the state of the art. Information Fusion 64, 71–91 (2020)

Article  Google Scholar 

Wang, K., Zheng, M., Wei, H., Qi, G., Li, Y.: Multi-modality medical image fusion using convolutional neural network and contrast pyramid. Sensors 20(8), 2169 (2020)

Article  PubMed  PubMed Central  Google Scholar 

Jose, J., Gautam, N., Tiwari, M., Tiwari, T., Suresh, A., Sundararaj, V., Rejeesh, M.: An image quality enhancement scheme employing adolescent identity search algorithm in the nsst domain for multimodal medical image fusion. Biomedical Signal Processing and Control 66, 102480 (2021)

Article  Google Scholar 

Liu, S., Wang, M., Yin, L., Sun, X., Zhang, Y.-D., Zhao, J.: Two-scale multimodal medical image fusion based on structure preservation. Frontiers in Computational Neuroscience 15, 133 (2022)

Article  Google Scholar 

Ye, F., Li, X., Zhang, X.: Fusioncnn: a remote sensing image fusion algorithm based on deep convolutional neural networks. Multimedia Tools and Applications 78, 14683–14703 (2019)

Article  Google Scholar 

Huang, M., Liu, S., Li, Z., Feng, S., Wu, D., Wu, Y., Shu, F.: Remote sensing image fusion algorithm based on two-stream fusion network and residual channel attention mechanism. Wireless Communications and Mobile Computing 2022, 1–14 (2022)

CAS  Google Scholar 

Wang, W., Han, C., Zhou, T., Liu, D.: Visual recognition with deep nearest centroids. arXiv preprint arXiv:2209.07383 (2022)

Salahuddin, Z., Woodruff, H.C., Chatterjee, A., Lambin, P.: Transparency of deep neural networks for medical image analysis: A review of interpretability methods. Computers in biology and medicine 140, 105111 (2022)

Article  PubMed  Google Scholar 

Fan, F.-L., Xiong, J., Li, M., Wang, G.: On interpretability of artificial neural networks: A survey. IEEE Transactions on Radiation and Plasma Medical Sciences 5(6), 741–760 (2021)

Article  PubMed  PubMed Central  Google Scholar 

Dong, S., Gao, Z., Pirbhulal, S., Bian, G.-B., Zhang, H., Wu, W., Li, S.: Iot-based 3d convolution for video salient object detection. Neural computing and applications 32, 735–746 (2020)

Article  Google Scholar 

Shang, R., Chen, C., Wang, G., Jiao, L., Okoth, M.A., Stolkin, R.: A thumbnail-based hierarchical fuzzy clustering algorithm for sar image segmentation. Signal Processing 171, 107518 (2020)

Article  Google Scholar 

Venkatanath, N., Praneeth, D., Bh, M.C., Channappayya, S.S., Medasani, S.S.: Blind image quality evaluation using perception based features. In: 2015 Twenty First National Conference on Communications (NCC), pp. 1–6 (2015). IEEE

Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Transactions on image processing 21(12), 4695–4708 (2012)

Article  PubMed  Google Scholar 

Mittal, A., Soundararajan, R., Bovik, A.C.: Making a completely blind image quality analyzer. IEEE Signal processing letters 20(3), 209–212 (2012)

Article  Google Scholar 

Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: European Conference on Computer Vision, pp. 740–755 (2014). Springer

Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)

Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022 (2021)