Compared　with　the　conventional　RGB　image　and　panchromatic　image,　hyperspectral　image　can　provide　more　details　and　features　with　the　additional　spectral　dimension.　Nowadays,　hyperspectral　image　has　been　applied　into　various　computer　vision　tasks,　such　as　classification,　medical　diagnosis,　face　recognition,　objects　tracking,　and　so　on.　In　order　to　obtain　hyperspectral　image,　traditional　imaging　systems　capture　the　3D　information　with　scanning　techniques.　Such　scanning　based　imaging　systems　can　only　record　the　spectral　information　of　one　or　a　few　scene　points　at　the　same　time,　which　inevitably　suffers　from　the　tradeoff　between　spectral　resolution　and　time　effciency.　Thanks　to　the　flourish　of　computational　photography,　snapshot　spectral　imagers　have　been　developed　to　overcome　the　drawback　of　conventional　imaging　systems　in　recent　years.　Imaging　systems　in　this　category　have　the　ability　to　capture　the　full　hyperspectral　image　with　one　single　exposure.　Leveraging　the　compressive　sensing　(CS)　theory,　coded　aperture　snapshot　spectral　imaging　(CASSI)　stands　out　as　a　promising　solution　among　those　systems.　With　elaborate　optical　design,　CASSI　encodes　the　3D　hyperspectral　image　into　the　2D　compressive　measurement　and　then　reconstruct　the　underlying　image　with　the　CS　theory.　Incorporating　a　CASSI　system　and　a　color　detector,　the　dual-camera　compressive　hyperspectral　imager　can　promote　the　reconstruction　accuracy　of　CASSI　efficiently　and　thus　owns　broad　application　prospects.　How　to　reconstruct　hyperspectral　image　from　the　compressive　measurement　with　high　quality　is　an　urgent　problem　to　be　solved　for　the　system.　The　existing　methods　exploit　a　single　prior　information　of　hyperspectral　image　to　develop　reconstruction　algorithms,　which　fail　to　make　full　use　of　the　measurement　of　dual-camera　design,　and　the　reconstruction　quality　is　not　ideal　enough.　Based　on　the　strong　correlation　between　hyperspectral　image　and　its　corresponding　color　image　in　spatial　structure　and　spectral　response,　we　propose　a　color　adaptive　dictionary　based　reconstruction　method　to　improve　the　reconstruction　quality.　First,　in　the　case　of　introducing　non-negative　constraints　to　dictionary　elements　and　sparse　representation　coefficients,　three　over-complete　dictionaries　are　learned　from　the　color　measurement.　Considering　the　fact　that　a　single　band　of　hyperspectral　image　owns　very　high　texture　and　structure　similarity　with　its　corresponding　color　measurement,　we　utilize　the　color　measurement　to　learn　over-complete　dictionaries　for　sparse　reconstruction.　Second,　based　on　the　spectral　response　of　the　color　camera,　a　suitable　dictionary　with　high　spectral　correlation　is　selected　for　each　band.　Specifically,　we　choose　the　dictionary　of　the　channel　with　the　largest　response　amplitude　of　the　RGB　camera　in　the　current　spectral　band　as　its　sparse　basis,　so　as　to　ensure　the　high　sparsity　of　the　sparse　representation　and　improve　the　reconstruction　quality.　Then,　by　integrating　the　sparse　representation　with　the　system　imaging　principle,　we　develop　an　optimization　framework　for　hyperspectral　image　reconstruction,　which　is　finally　solved　via　the　alternative　direction　multiplier　method.　At　last,　we　conduct　a　thorough　experiment　on　both　the　hyperspectral　and　the　remote　sensing　data　sets　to　validate　the　performance　of　our　method.　Simulation　results　suggest　that　the　proposed　method　can　greatly　improve　the　reconstruction　fidelity　of　the　dual-camera　compressive　hyperspectral　imager,　which　verifies　the　practical　application　potential　of　our　method.　©　2020,　Science　Press.　All　right　reserved.