Most　industrial　Internet　of　Things　(IoT)　devices　reduce　the　capture　image　size　using　high-ratio　JPEG　compression,　saving　storage　space　and　transmission　bandwidth　consumption.　However,　the　resulting　compression　artifacts　considerably　affect　the　accuracy　of　subsequent　tasks.　Most　artifact　reduction　algorithms　do　not　consider　the　limitations　of　storage　space　and　computing　power　of　edge　devices.　In　this　study,　a　blind　artifact　reduction　recurrent　network　(BARRN),　which　can　reduce　compression　artifacts　when　the　quality　factors　are　unknown,　is　proposed.　First,　a　structure　based　on　recurrent　convolution　is　designed　for　the　specific　requirements　of　industrial　IoT　image　acquisition　devices;　the　network　can　be　scaled　according　to　system　resource　constraints.　Second,　a　more　efficient　convolution　group,　capable　of　adaptively　processing　different　degradation　levels,　is　proposed　for　optimal　use　of　the　limited　computational　resources.　The　experimental　results　demonstrate　that　the　proposed　BARRN　can　meet　the　needs　of　industrial　systems　with　high　computational　efficiency.　IEEE