Multi-focus image fusion using dictionary-based sparse representation

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Pixel-level image fusion: A survey of the state of the art (2017, background): “In [50–53], an over-complete dictionary is learned from a large set of training samples similar to the input images, thus adding adaptability for the representation.”
Rethinking the Image Fusion: A Fast Unified Image Fusion Network based on Proportional Maintenance of Gradient and Intensity (2020, methodology): “…provided by (Cai, Gu, and Zhang 2018) for task2; MRI and PET images from Harvard medical school web-site 3 for task3; the dataset provided by (Nejati, Samavi, and Shirani 2015) and sametaymaz 4 for task4; and the panchromatic and multi-spectral images generated by the Quickbird satellite for…”
Multi-focus image fusion with a deep convolutional neural network (2017, methodology): “Similar to [30] , we also take advantage of the guided ﬁlter to improve the quality of initial decision map.”
SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer (2022, methodology): “Because of the number limitation of the Harvard medical dataset, we randomly select 20 test images for all medical image fusion tasks, which is consistent with the Lytro dataset.”