Compressed　sensing　in　image　reconstruction　has　attracted　attention　and　many　studies　are　proposed.　As　we　know,　adding　prior　knowledge　about　the　distribution　of　the　support　on　the　original　signal　to　CS　can　improve　the　quality　of　reconstruction.　However,　it　is　still　difficult　for　a　recovery　framework　adjusts　its　strategy　for　exploiting　the　prior　knowledge　efficiently　according　to　the　current　estimated　signals　in　serial　iterations.　With　the　theory　of　information　geometry,　we　propose　an　adaptive　strategy　based　on　the　current　estimated　signal　in　each　iteration　of　the　recovery.　We　also　improve　the　performance　of　existing　algorithms　through　the　adaptive　strategy　for　exploiting　the　prior　knowledge　according　to　the　current　estimated　signal.　Simulations　are　presented　to　validate　the　results.　In　the　end,　we　also　show　the　application　of　the　model　in　the　image.