A　novel　approach　was　presented　to　enhance　the　capability　of　resolving　thin　coating　layers　using　terahertz　pulsed　imaging　(TPI)　based　on　a　neural　network-based　hybrid　signal　procession　method,　which　is　of　great　significance　for　in-line　painting　applications.　In　the　present　work,　Terahertz　detected　signals　were　obtained　by　numerical　simulation　using　finite　difference　time　domain　(FDTD)　method.　Models　of　marine　protective　coatings　with　different　coating　structures　were　calculated　and　analyzed.　Different　signal　pre-processing　techniques,　including　Fourier　deconvolution,　Fast　Fourier　Transform　and　wavelet　analysis,　were　employed　on　the　terahertz　signals　respectively　to　obtain　various　signal　features.　The　processed　signal　was　subsequently　adopted　as　the　input　vectors　for　a　neural　network　(NN).　The　optimization　procedure　for　determining　the　architecture　of　neural　network　was　investigated　and　the　evaluated　results　obtained　by　the　different　networks　were　compared.　Furthermore,　the　predicted　results　of　thinner　coating　layer　obtained　by　multiple-regression　analysis　method　and　BP　network　prediction　method　respectively　were　compared.　The　analysis　demonstrated　that　the　best　prediction　performance　was　achieved　by　neural　network　technique　combined　with　wavelet　analysis.　Therefore,　the　hybrid　signal　processing　approach　could　be　recommended　for　terahertz　non-destructive　testing　applications　of　marine　protective　coating.