The(1u)　flare　stack　is　the　last　line　of　defense　in　the　safe　production　of　large-scale　chemical　plants.　Monitoring　black　smoke　produced　by　the　incomplete　flare　stack　exhaust　combustion　can　effectively　reduce　environmental　pollution　and　production　accident.　In　order　to　improve　the　ability　to　recognition　and　analyze　the　black　smoke,　high-resolution　flare　stack　scene　images　are　in　urgent　need.　To　this　end,　we　in　this　paper　propose　a　super-resolution　algorithm　based　on　convolutional　network　that　focuses　only　on　smoke　area　for　the　purpose　of　identifying　the　smoke　of　flare　stack.　With　a　lightweight　convolutional　neural　network　structure,　our　network　specializes　in　learning　smoke　characteristics　mapping　between　the　low-resolution　images　and　the　associated　high-resolution.　To　verify　validity,　our　algorithm　compares　the　super-resolution　quality　of　the　smoky　region　of　the　flare　stack　image　with　several　classic　super-resolution　algorithms.　The　experimental　results　show　that　our　algorithm　is　superior　to　the　classical　algorithms　when　applied　to　smoke　images.