Adaptive　optics　(AO)　in　conjunction　with　subsequent　postprocessing　techniques　have　obviously　improved　the　resolution　of　turbulence-degraded　images　in　ground-based　astronomical　observations　or　artificial　space　objects　detection　and　identification.　However,　important　tasks　involved　in　AO　image　postprocessing,　such　as　frame　selection,　stopping　iterative　deconvolution,　and　algorithm　comparison,　commonly　need　manual　intervention　and　cannot　be　performed　automatically　due　to　a　lack　of　widely　agreed　on　image　quality　metrics.　In　this　work,　based　on　the　Laplacian　of　Gaussian　(LoG)　local　contrast　feature　detection　operator,　we　propose　a　LoG　domain　matching　operation　to　perceive　effective　and　universal　image　quality　statistics.　Further,　we　extract　two　no-reference　quality　assessment　indices　in　the　matched　LoG　domain　that　can　be　used　for　a　variety　of　postprocessing　tasks.　Three　typical　space　object　images　with　distinct　structural　features　are　tested　to　verify　the　consistency　of　the　proposed　metric　with　perceptual　image　quality　through　subjective　evaluation.　(C)　2016　Society　of　Photo-Optical　Instrumentation　Engineers　(SPIE)