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Hyperspectral images (HSI) with abundant spectral information reflectedmaterials property usually perform low spatial resolution due to the hardwarelimits. Meanwhile, multispectral images (MSI), e.g., RGB images, have a highspatial resolution but deficient spectral signatures. Hyperspectral andmultispectral image fusion can be cost-effective and efficient for acquiringboth high spatial resolution and high spectral resolution images. Many of theconventional HSI and MSI fusion algorithms rely on known spatial degradationparameters, i.e., point spread function, spectral degradation parameters,spectral response function, or both of them. Another class of deeplearning-based models relies on the ground truth of high spatial resolution HSIand needs large amounts of paired training images when working in a supervisedmanner. Both of these models are limited in practical fusion scenarios. In thispaper, we propose an unsupervised HSI and MSI fusion model based on the cycleconsistency, called CycFusion. The CycFusion learns the domain transformationbetween low spatial resolution HSI (LrHSI) and high spatial resolution MSI(HrMSI), and the desired high spatial resolution HSI (HrHSI) are considered tobe intermediate feature maps in the transformation networks. The CycFusion canbe trained with the objective functions of marginal matching in singletransform and cycle consistency in double transforms. Moreover, the estimatedPSF and SRF are embedded in the model as the pre-training weights, whichfurther enhances the practicality of our proposed model. Experiments conductedon several datasets show that our proposed model outperforms all comparedunsupervised fusion methods. The codes of this paper will be available at thisaddress: https: //github.com/shuaikaishi/CycFusion for reproducibility.

Unsupervised Hyperspectral and Multispectral Images Fusion Based on the Cycle Consistency