Industrial　images　are　often　captured　under　full-time　and　full-weather　conditions,　leading　to　inevitable　noise　during　the　imaging　process,　which　can　impact　subsequent　detection　algorithms.　In　recent　years,　image　denoising　with　neural　networks　has　been　the　rapid　development.　However,　training　such　networks　typically　requires　a　large　dataset,　which　is　scarce　in　publicly　available　industrial　image　databases.　In　this　paper,　we　propose　a　novel　approach　termed　Zero-Shot　Industrial　Image　Lightweight　Denoising　(ZSILD)　network,　which　effectively　denoises　single　noisy　industrial　image　without　the　need　for　datasets.　First,　we　sample　the　paired　neighbour　pixels　of　a　random　noisy　industrial　image,　which　are　then　utilized　to　train　a　lightweight　denoising　network.　Second,　we　design　a　lightweight　depthwise　convolutions　network　based　on　bottleneck　residual　structure　with　shortcut　connections.　Finally,　this　network　is　trained　on　the　sampled　pairs　using　a　novel　loss　function　aimed　at　enhancing　denoising　performance.　Our　experiments　conduct　on　real-world　industrial　ambient　noise　demonstrate　that　our　ZSILD　method　outperforms　existing　denoising　techniques,　all　while　requiring　comparatively　minimal　computational　resources.　©　2024　SPIE.　All　rights　reserved.