A　fast　deblurring　network,　based　on　a　high-performance　convolutional　network　and　pixel　volume,　is　proposed　to　address　the　limitations　of　existing　video　deblurring　algorithms,　which　often　overly　emphasize　inter-frame　information,　leading　to　high　algorithmic　complexity.　First,　high-performance　convolutional　networks　are　utilized　to　prune　the　deblurring　network,　thereby　reducing　both　the　number　of　model　parameters　and　computational　complexity.　To　address　the　increased　network　computational　complexity　resulting　from　the　extensive　use　of　traditional　two-dimensional　convolutional　layers,　depthwise　over-parameterized　convolutions　are　employed　to　replace　traditional　convolutions.　This　substitution　significantly　reduces　computational　complexity　without　compromising　the　network＇s　structure　and　performance.　In　addition,　the　Charbonnier　loss　function　is　used　to　approximate　the　mean　absolute　error　(MAE)　loss　function　to　alleviate　the　over-smoothing　problem.　At　the　same　time,　the　problem　of　non-differentiability　of　the　MAE　loss　function　at　zero　is　solved　by　adding　a　constant,　to　enhance　the　visual　quality　of　video　images.　Experimental　results　demonstrate　that　the　proposed　method　delivers　superior　deblurring　performance.　Compared　with　the　baseline　pixel　volume　deblurring　network　framework,　our　method　achieves　a　significant　reduction　in　model　complexity,　demonstrating　28.73%　fewer　parameters　and　59.96%　lower　floating-point　operations,　underscoring　its　theoretical　significance.　(c)　2025　SPIE　and　IS&T