This　paper　proposes　a　novel　blind　hyperspectral　image　super　resolution　method.　The　proposed　method　can　estimate　simultaneously　the　unknown　hyperspectral　image　and　the　blur　kernel　based　on　the　linear　spectral　unmixing　technique.　The　total　variation　term　is　used　for　the　blur　kernel　regularization　and　simultaneous　total　variation　and　sparse　representation　are　used　for　abundance　regularization　terms.　Because　the　image　and　blur　kernel　are　simultaneously　estimated　with　the　double　reg-ularization　terms　introduced　for　abundance,　the　estimation　error　can　be　minimized　so　that　the　performance　of　the　proposed　method　can　be　improved.　Finally,　the　proposed　optimization　formulation　is　effectively　solved　by　block　coordinate　descent　method.　Experimental　results　show　that　the　proposed　method　is　effective　and　superior　to　existing　blind　hyperspectral　image　super　resolution　approach　in　terms　of　reconstruction　quality.　©　2017　IEEE.