An autoencoder deep residual network model for multi focus image fusion
Author:
Funder
Centre for Engineering Research and Development
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-16991-6.pdf
Reference55 articles.
1. Liu, Y, Liu, S and Wang, Z (2015) Multi-focus image fusion with dense SIFT. Inf Fusion, 23(C):139–155. https://doi.org/10.1016/j.inffus.2014.05.004
2. De, I, Chanda, B (2013) Multi-focus image fusion using a morphology-based focus measure in a quad-tree structure. Inf Fusion, 14(2):136–146. https://doi.org/10.1016/j.inffus.2012.01.007
3. Li, M, Cai, W and Tan, Z (2006) A region-based multi-sensor image fusion scheme using pulse-coupled neural network. Pattern Recognit Lett, 27(16):1948–1956. https://doi.org/10.1016/j.patrec.2006.05.004
4. Zhang, Q, Shi, T, Wang, F, Blum, RS and Han, J (2018) Robust sparse representation based multi-focus image fusion with dictionary construction and local spatial consistency. Pattern Recognit, 83(C)299–313, https://doi.org/10.1016/j.patcog.2018.06.003
5. Zhang, Q, Liu, Y, Blum, RS, Han, J, Tao, D (2018) Sparse representation based multi-sensor image fusion for multi-focus and multi-modality images: A review. Inf Fusion, 40:57–75. https://doi.org/10.1016/j.inffus.2017.05.006
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