In　the　field　of　infrared　(IR)　polarization　imaging,　division-of-focal-plane　(DoFP)　polarization　sensors　have　gained　prominence　for　their　compact　design　and　real-time　capture　of　multiple　polarization　states,　crucial　for　industrial　inspection　and　target　recognition.　Nonetheless,　the　spatial　resolution　of　a　singular　DoFP　image,　particularly　when　depicting　a　single　polarization　state,　is　inherently　constrained　by　the　sensing　mechanism.　This　limitation　greatly　reduces　the　effectiveness　of　such　imagery　incomplex　environments　where　high-definition　details　are　essential　for　accurate　analysis.　Presently,　super-resolution　(SR)　techniques　for　DoFP　IR　polarization　images　is　underdeveloped.　This　study　innovatively　introduces　a　multi-aperture　IR　polarization　SR　algorithm,　achieved　by　merging　Alternating　Direction　Method　of　Multipliers　-　Block　Matching　and　3D　Collaborative　Filtering　(ADMM-BM3D)　denoising　with　maximum　likelihood　estimation.　In　this　proposed　methodology,　IR　polarization　images　from　a　multi-aperture　imaging　system　are　decomposed　into　sub-images　representing　distinct　polarization　states.　Subsequently,　sub-pixel　precision　image　registration　aligns　peripheral　images　with　the　central　aperture.　Maximum　likelihood　estimation　reconstructs　high-resolution　single-polarization　sub-images　using　the　intrinsic　sub-pixel　displacement　data　from　the　multi-aperture　system.　The　generated　SR　images　are　refined　through　ADMM-BM3D　denoising.　Evaluations　in　varied　real-world　scenarios,　including　indoor　and　outdoor　environments,　benchmark　our　method　against　state-of-the-art　DoFP　image　SR　techniques.　Our　method　shows　superiority　in　no-reference　image　quality　assessment　indices　like　the　Natural　Image　Quality　Evaluator　(NIQE)　and　the　Blind/Referenceless　Image　Spatial　Quality　Evaluator　(BRISQUE).　Empirical　results　confirm　the　method＇s　ability　to　improve　visual　quality　and　detail　clarity　while　preserving　the　integrity　of　polarization　information.　©　2025　SPIE.