Ferrography　is　a　technology　that　can　be　applied　in　inspecting　features　of　wear　particles　in　machines　and　inferring　their　health　status.　With　the　development　of　online　ferrography,　which　employs　image　processing　to　captured　wear　particle　images,　the　inspection　process　has　become　automatic.　However,　it　is　found　that　images　captured　often　contain　out-of-focus　degradations　and　low　brightness.　A　restoration　framework　is　here　proposed　to　mitigate　this　problem.　The　main　idea　is　to　extract　object　edges,　magnify　with　a　non-linear　gain　factor,　then　combine　with　the　input　image　to　produce　an　enhanced　image　to　facilitate　further　analysis.　Parameters　adopted　in　the　process　are　optimized　using　a　metaheuristic　search　where　the　image　information　content　and　brightness　are　maximized.　Experimental　results,　obtained　from　processing　real-world　wear　particle　images　in　lubricant　circuits,　have　shown　qualitative　and　quantitative　improvements　over　the　input　images.