Hyperspectral　images　(HSI)　with　high　spatial　and　spectral　resolutions　have　many　applications　in　astronautics,　re-mote　sensing,　and　so　on.　However,　it　is　challenging　to　obtain　HSI　with　existing　imaging　techniques　due　to　hardware　limitations.　In　most　cases,　high-resolution　multispectral　(HrMS)　images　or　low-resolution　hyperspectral　(LrHS)　images　are　obtained.　Therefore,　the　fusion　of　HrMS　images　and　LrHS　images　for　HSI　super-resolution　has　attracted　widespread　attention.　In　this　paper,　we　propose　a　network　denoted　as　a　model-based　deep　unfolding　net-work(DuFNet)　for　hyperspectral　image　super-resolution　(HSSR)　task　with　clear　interpretability.　Specifically,　we　integrate　the　ISTA-Net　into　a　well-established　fusion　network　that　is　MHF-Net　to　fully　take　advantage　of　the　generalization　of　the　ISTA-Net.　Experimental　results　demonstrate　that　the　proposed　NAM-DuFNet　outperforms　existing　state-of-the-art　methods　in　terms　of　subjective　and　objective　results.　©　2022　IEEE.