The　quality　of　infrared　images　is　affected　by　various　degradation　factors,　such　as　image　blurring　and　noise　pollution.　Anisotropic　total　variation　(ATV)　has　been　shown　to　be　a　good　regularization　approach　for　image　deblurring.　However,　there　are　two　main　drawbacks　in　ATV.　First,　the　conventional　ATV　regularization　just　considers　the　sparsity　of　the　first-order　image　gradients,　thus　leading　to　staircase　artifacts.　Second,　it　employs　the　L1-norm　to　describe　the　sparsity　of　image　gradients,　while　the　L1-norm　has　a　limited　capacity　of　depicting　the　sparsity　of　sparse　variables.　To　address　these　limitations　of　ATV,　a　high-order　total　variation　is　introduced　in　the　ATV　deblurring　model　and　the　Lp-pseudonorm　is　adopted　to　depict　the　sparsity　of　low-and　high-order　total　variation.　In　this　way,　the　recovered　image　can　fit　the　image　priors　with　clear　edges　and　eliminate　the　staircase　artifacts　of　the　ATV　model.　The　alternating　direction　method　of　multipliers　is　used　to　solve　the　proposed　model.　The　experimental　results　demonstrate　that　the　proposed　method　does　not　only　remove　blurs　effectively　but　is　also　highly　competitive　against　the　state-of-the-art　methods,　both　qualitatively　and　quantitatively.　©　2020　by　the　authors.