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DISTA-Net++: Rethinking Infrared Small Target Unmixing Beyond Sub-Pixel Separation

Paper recorded by Signals 4 on 2026-09-16 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-16 on arXiv · recorded by Signals 4 on 2026-09-17

Category: cs.CV · 计算机视觉 · first seen 2026-09-17

Abstract

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

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#23 most recent of 237 cs.CV papers we have recorded · ↑ newer: Copy What Is Seen, Generate What Is Not: Training-Free Anomaly-Aware V · ↓ older: Toward Markerless Video-based Tremor Analysis: Objective Quantificatio
Cite this page: DISTA-Net++: Rethinking Infrared Small Target Unmixing Beyond Sub-Pixel Separation: the #23 most recent of 237 cs.CV papers we have recorded (as of 2026-09-16). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/dista-net-rethinking-infrared-small-target-unmixing-beyond-sub-pixel-separation.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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