Multifocus and multispectral image fusion based on pixel significance using discrete cosine harmonic wavelet transform

By B. K. Shreyamsha Kumar. Signal, Image and Video Processing, 2012

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Infrared and Visible Image Fusion using a Deep Learning Framework (2018, methodology): “The graph plot of Nabf for all fused images is shown in Fig.7.”
Image fusion based on pixel significance using cross bilateral filter (2013, background): “Further, the superior/similar fusion performance is achieved with discrete cosine harmonic wavelet [25] with reduced computational complexity than that in Naidu [24].”
NestFuse: An Infrared and Visible Image Fusion Architecture Based on Nest Connection and Spatial/Channel Attention Models (2020, background): “In Fig.9, at ﬁrst 400 iterations, the auto-encoder network has rapid convergence with the increase of the parameter λ .”
DDcGAN: A Dual-Discriminator Conditional Generative Adversarial Network for Multi-Resolution Image Fusion (2020, methodology): “From top to bottom: PET image, MRI image, fusion results of DCHWT [55], DDCTPCA [14], ASR [56], Structure-Aware [57], FusionGAN [21] and RCGAN [58] and our DDcGAN.”
SDNet: A Versatile Squeeze-and-Decomposition Network for Real-Time Image Fusion (2021, background): “A smaller N AB/F value indicates less artifacts and contrast distortion.”
Rethinking the Image Fusion: A Fast Unified Image Fusion Network based on Proportional Maintenance of Gradient and Intensity (2020, methodology): “The average run time of DCHWT, ASR, PCA and our PMGI is about 0.8636, 23.6055, 0.0046, and 0.0150 (unit: second).”