In　image　restoration　problems,　it　is　reasonable　to　add　nonnegative　constraints　because　of　the　physical　meaning　of　images.　In　general,　this　problem　can　be　expressed　as　a　quadratic　programming　problem　with　nonnegative　constraints,　which　results　in　a　linear　complementary　problem　from　the　KKT　optimization　conditions.　By　reformulating　the　linear　complementary　problem　as　implicit　fixed-point　equations,　a　class　of　modulus-based　matrix　splitting　iteration　methods　is　established.　In　this　paper,　for　a　better　computational　implementation,　we　present　an　inexact　iteration　process　for　these　modulus-based　methods.　Convergence　properties　for　this　inexact　process　are　analyzed,　and　some　specific　implementations　for　the　inner　iterations　are　presented.　Numerical　experiments　for　nonnegatively　constrained　image　restorations　are　presented,　and　the　results　show　that　our　methods　are　comparable　and　more　efficient　than　the　existing　projection　type　methods.