Natural　video　capturing　suffers　from　visual　blurriness　due　to　high-motion　of　cameras　or　objects.　Until　now,　the　video　blurriness　removal　task　has　been　extensively　explored　for　both　human　vision　and　machine　processing.　However,　its　computational　cost　is　still　a　critical　issue　and　has　not　yet　been　fully　addressed.　In　this　paper,　we　propose　a　novel　Lightweight　Video　Deblurring　(LightViD)　method　that　achieves　the　top-tier　performance　with　an　extremely　low　parameter　size.　The　proposed　LightViD　consists　of　a　blur　detector　and　a　deblurring　network.　In　particular,　the　blur　detector　effectively　separate　blurriness　regions,　thus　avoid　both　unnecessary　computation　and　over-enhancement　on　non-blurriness　regions.　The　deblurring　network　is　designed　as　a　lightweight　model.　It　employs　a　Spatial　Feature　Fusion　Block　(SFFB)　to　extract　hierarchical　spatial　features,　which　are　further　fused　by　ConvLSTM　for　effective　spatial-temporal　feature　representation.　Comprehensive　experiments　with　quantitative　and　qualitative　comparisons　demonstrate　the　effectiveness　of　our　LightViD　method,　which　achieves　competitive　performances　on　GoPro　and　DVD　datasets,　with　reduced　computational　costs　of　1.63M　parameters　and　96.8　GMACs.　Trained　model　available:　https://github.com/wgp/LightVid.　IEEE