Photon　counting　computed　tomography　(PCCT)　can　offer　substantial　benefits　over　conventional　energy-integrating　CT　due　to　its　high-speed　semiconductors.　However,　the　PCCT　splits　the　transmitted　spectrum　into　multiple　bins,　leading　to　a　relatively　low　signal-to-noise　ratio　in　each　energy　bin　and　then　the　reconstructed　PCCT　images　suffer　from　noise.　Most　of　existing　PCCT　image　restoration　methods　assume　that　the　noise　in　the　PCCT　images　is　independent　and　identically　distributed　(i.i.d).　This　might　produce　bias　in　the　results　because　the　noise　distribution　is　much　more　complicated.　In　this　work,　we　model　the　noise　in　the　PCCT　image　via　i.i.d　mixture　of　Gaussian　(MOG)　noise　assumption,　which　can　successfully　characterize　various　shape　in　the　PCCT　images.　Then　the　i.i.d　MOG　model　is　introduced　into　a　PCCT　image　restoration　approach.　The　prior　information　(an　average　image　from　the　PCCT　images　at　multiple　bins)　is　added　to　the　restoration　approach　to　further　promote　performance.　Therefore,　the　presented　restoration　approach　can　be　termed　as　pMOG.　Then,　an　effective　optimization　algorithm　is　designed　to　solve　the　presented　pMOG　approach.　The　experimental　results　with　simulation　study　demonstrate　that　the　presented　pMOG　approach　can　effectively　suppress　noise　and　preserve　resolution　compared　to　the　non-local　means　approach.　©　2018　IEEE.