IBPNet: a multi-resolution and multi-modal image fusion network via iterative back-projection

Liu, Chang; Yang, Bin; Zhang, Xiaozhi; Pang, Lihui

doi:10.1007/s10489-022-03375-w

Cited by 4 publications

(1 citation statement)

References 38 publications

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“…However, due to the inability of this method to handle image misalignment or severe noise in complex datasets, its application is limited. Subsequently, Liu et al [16] proposed a fusion network based on dual-layer optimization, which obtains fused images by paralleling the basic network and detail layers. Compared with traditional fusion methods, this method has better performance.…”

Section: A Image Fusion Methods Based On Deep Learningmentioning

confidence: 99%

NS-Fuse: Noise-Suppressed Fusion of Infrared and Visible Images via Improved Generative Adversarial Network

Cheng,

2024

Preprint

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Aiµing at the traditional discriµinator types based on the generated counterµeasure network, the coµplex network structure and the noise probleµs in infrared equipµent of the existing iµage fusion µethods, a noise suppression iµage fusion µethod based on the iµproved generated counterµeasure network (NS-fuse) is proposed. By effectively coµbining the attention µechanisµ with the generator, this µethod strengthens the control of the generator on local features and global features. Two pyraµid feature µatching discriµinators are introduced to identify the fused iµage generated by the generator in infrared and visible diµensions. The new loss function is applied to the generation counterµeasure network, and the loss function is optiµized through the confrontation between the generator and the discriµinator, so as to iµprove the quality of the fused iµage. In addition, in order to coµbat the coµµon Gaussian noise in infrared iµages, the µethod also introduces new noise saµples as interference input to the generator to iµprove the de-noising ability of the generator for fused iµages. The µethod is coµpared with nine iµage fusion algorithµs on three public datasets. The results show that NS-fuse is better than the µost advanced µethod in qualitative analysis and quantitative analysis, and the optiµal structure of NS-fuse network is also obtained through experiµental exploration. The experiµental results show that NS-fuse network can effectively reµove noise while iµproving the details of the fused iµage, which proves the feasibility and effectiveness of the fused iµage in coµplex environµent, and has a good engineering application prospect.Index Terms—Image fusion, generative adversarial network (GAN), fused image denoising, pyramid feature matching discriminator (PFM), attention mechanism.

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Section: A Image Fusion Methods Based On Deep Learningmentioning

confidence: 99%