Conventional　hierarchical　image　representation　methods,　e.g.　Wavelet　transform,　use　pre-determined　filter　banks　which　lack　in　adaption　to　the　variant　statistical　characteristics　of　images.　In　this　paper,　we　propose　learning　adaptive　filter　banks　for　hierarchical　sparse　image　representation　with　a　wavelet-like　compact　form　using　a　deconvolutional　network.　The　proposed　scheme　is　verified　by　evaluating　its　sparsity　in　image　representation.　Experimental　results　demonstrate　that　the　proposed　scheme　outperforms　9/7　and　5/3　wavelets　transform　in　terms　of　both　objective　and　subjective　qualities　under　the　same　sparsity.