The　flare,　which　aims　to　burn　all　kinds　of　waste　gas　produced　by　petrochemical　enterprises,　is　an　important　facility　for　plants　to　keep　safe　and　prevent　harming　the　environment.　However,　when　the　exhaust　gas　is　not　sufficiently　burned,　black　smoke　will　be　produced　in　the　flare　system　and　endanger　air　quality　and　human　health.　So,　as　a　crucial　task,　reducing　the　emission　of　flare　black　smoke　is　attracting　a　growing　amount　of　research　attention.　In　a　typical　flare　smoke　reduction　system,　a　high-resolution　smoke　image　is　greatly　beneficial　to　smoke　recognition　and　analysis.　But　the　smoke　image　usually　encounters　the　low-resolution　problem.　Accordingly,　in　this　paper　we　propose　a　super-resolution　method　specific　for　smoke　images,　which　is　called　smoke　images　upsampling　method　(SIUM).　Considering　the　texture　and　edge　characteristics　of　the　smoke　images,　the　proposed　SIUM　learns　a　mapping　between　the　low-resolution　images　and　the　associated　high-resolution　images.　Experimental　results　demonstrate　that　our　proposed　SIUM　is　prominently　superior　to　relevant　state-of-the-art　technologies　when　applied　to　upsample　low-resolution　smoke　images.