Current　hyperspectral　imaging　(HSI)　technologies　struggle　with　prolonged　acquisition　time,　bulky　system　design,　and　high-computational　complexity.　We　propose　aperture　array-based　self-supervised　hyperspectral　super-resolution　(SR)　imaging,　a　novel　computational　HSI　framework　that　integrates　an　aperture　array　optical　structure　with　a　physics-driven,　self-supervised　deep　learning　model.　The　aperture　array　enables　spectral　division　while　leveraging　aperture　parallax　for　spatial　SR,　achieving　high　spectral　fidelity　and　spatial　SR　in　a　compact　form　factor.　To　fully　exploit　the　imaging　characteristics　of　the　proposed　system,　we　design　a　divide-and-conquer　self-supervised　hyperspectral　SR　imaging　(DSHSI)　algorithm,　which　is　data-efficient,　noniterative,　and　free　of　large-scale　ground-truth　datasets.　Unlike　existing　hyperspectral　restoration　methods　that　suffer　from　long　inference　times　and　poor　generalization,　DSHSI　integrates　physics-driven　priors　into　a　self-supervised　learning　framework,　enabling　robust　performance　on　unseen　scenes　and　out-of-distribution　degradations.　DSHSI　jointly　performs　denoising,　SR,　and　deblurring　in　a　unified　manner,　reducing　runtime　by　25×　,　FLOPs　by　9×　,　and　model　parameters　by　3.6×　than　DualSR　on　public　remote　sensing　datasets,　while　achieving　a　1.9　dB　PSNR　gain,　enabling　fast　and　efficient　hyperspectral　reconstruction.　In　the　optical　experiments,　it　achieves　the　best　NIQE　and　the　spectral　distortion　metric　that　is　closely　aligned　with　the　spectral　response　of　observed　HSI　data.　©　1980-2012　IEEE.