The　fixed-pattern　noise　(FPN)　caused　by　nonuniform　optoelectronic　response　limits　the　sensitivity　of　an　infrared　imaging　system　and　severely　reduces　the　image　quality.　Therefore,　nonuniform　correction　of　infrared　images　is　very　important.　In　this　paper,　we　propose　a　deep　filter　neural　network　to　solve　the　problems　of　network　underfitting　and　complex　training　with　convolutional　neural　network　(CNN)　applications　in　nonuniform　correction.　Our　work　is　mainly　based　on　the　idea　of　deep　learning,　where　the　nonuniform　image　noise　features　are　fully　learned　from　a　large　number　of　simulated　training　images.　The　network　is　designed　by　introducing　the　filter　and　the　subtraction　structure.　The　background　interference　of　the　image　is　removed　by　the　filter,　so　the　learning　model　is　gathered　in　the　nonuniform　noise.　The　subtraction　structure　is　used　to　further　reduce　the　input-to-output　mapping　range,　which　effectively　simplifies　the　training　process.　The　results　from　the　test　on　infrared　images　shows　that　our　algorithm　is　superior　to　the　state-of-the-art　algorithm　in　visual　effects　and　quantitative　measurements,　providing　a　new　method　for　deep　learning　in　nonuniformity　correction　of　single　images.