Abstract
Infrared image super-resolution is currently led by Mamba-based networks with 26 to 37 million parameters, which are difficult to deploy on the airborne and handheld platforms where thermal imaging is most needed. This paper presents TIRMamba, a network with 896K to 910K parameters for single-channel thermal imagery. A Thermal Prior Highway computes gradient, local-contrast and spectral cues once at the input and, through one adapter per residual group, modulates a weight-tied bidirectional state-space trunk and gates its dual-scale detail branch; a tri-path reconstruction adds the learned residual to a bicubic radiometric baseline. Because the standard benchmark provides only 265 infrared training images and evaluates fusion products on full images, we train with a replay strategy: grayscale DIV2K pre-training followed by fine-tuning on 64-pixel patches drawn with equal probability from the infrared and natural corpora. At scale factor 4, TIRMamba matches the strongest protocol-trained methods on both official test sets with 29 to 40 times fewer parameters and 2.8 to 9.4 times lower latency; at scale factor 2 it gives the highest SSIM on both. A variant with prior-conditioned selectivity, TIRMamba-Rad, corrects a 3 dB raw-thermal failure of an intermediate size-invariant design and gives the best results at scale factor 4 on raw-thermal, unmanned-aerial-vehicle and independent-sensor test sets. Code and trained models will be released at https://github.com/julian135707/TIRMamba upon acceptance.
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Publication details
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Cite this article
APA 7
Lin, C. A., Liu, T. J., & Ouyang, Y. C. (2026). TIRMamba: A Thermal-Prior-Modulated State-Space Network for Sub-Million-Parameter Infrared Image Super-Resolution. https://omanscience.com/en/articles/tirmamba-a-thermal-prior-modulated-state-space-network-for-sub-million-parameter-infrared-image-super-resolution
MLA 9
Lin, Chun-An, et al. "TIRMamba: A Thermal-Prior-Modulated State-Space Network for Sub-Million-Parameter Infrared Image Super-Resolution." https://omanscience.com/en/articles/tirmamba-a-thermal-prior-modulated-state-space-network-for-sub-million-parameter-infrared-image-super-resolution.
Chicago (author–date)
Lin, Chun-An, Tsung-Jung Liu, and Yen-Chieh Ouyang. 2026. "TIRMamba: A Thermal-Prior-Modulated State-Space Network for Sub-Million-Parameter Infrared Image Super-Resolution." https://omanscience.com/en/articles/tirmamba-a-thermal-prior-modulated-state-space-network-for-sub-million-parameter-infrared-image-super-resolution.
Harvard
Lin, C. A., Liu, T. J. and Ouyang, Y. C. (2026) 'TIRMamba: A Thermal-Prior-Modulated State-Space Network for Sub-Million-Parameter Infrared Image Super-Resolution', Available at: https://omanscience.com/en/articles/tirmamba-a-thermal-prior-modulated-state-space-network-for-sub-million-parameter-infrared-image-super-resolution.
Vancouver
Lin CA, Liu TJ, Ouyang YC. TIRMamba: A Thermal-Prior-Modulated State-Space Network for Sub-Million-Parameter Infrared Image Super-Resolution. https://omanscience.com/en/articles/tirmamba-a-thermal-prior-modulated-state-space-network-for-sub-million-parameter-infrared-image-super-resolution
IEEE
C. A. Lin, T. J. Liu, and Y. C. Ouyang, "TIRMamba: A Thermal-Prior-Modulated State-Space Network for Sub-Million-Parameter Infrared Image Super-Resolution," https://omanscience.com/en/articles/tirmamba-a-thermal-prior-modulated-state-space-network-for-sub-million-parameter-infrared-image-super-resolution.