DISTA-Net++: Rethinking Infrared Small Target Unmixing Beyond Sub-Pixel Separation
Long-range infrared imaging frequently confronts dense target clusters whose diffraction-limited signatures merge into a single indistinguishable blob, concealing the number, sub-pixel positions, and radiant intensities of the underlying sources. While deep learning has advanced general object detection, resolving such Closely-Spaced Infrared Small Targets (CSIST) remains largely unexplored, owing to a systemic infrastructure void and a fundamental paradigm mismatch. The dominant formulation, which reduces unmixing to a blind, discrete sub-pixel separation, is inherently insufficient: without semantic guidance, the ill-posed inverse problem admits ambiguous solutions plagued by false and ...
| Authors | Mengze Xu, Zhu Liu, Weidong Sheng et al. |
|---|---|
| Primary category | cs.CV |
| Published | 16 September 2026 |
| Last updated at source | 16 September 2026 |
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