Infrared small target detection (IRSTD) supports remote-sensing surveillance and early warning, but infrared targets often occupy only a few pixels, exhibit weak appearance cues, and resemble thermal clutter. Vision-language detectors such as Grounding DINO provide a flexible prompt-driven detection interface, but direct transfer to IRSTD faces three mismatches: a single prompt cannot describe target appearance diversity, generic decoder queries are not calibrated for weak scale-sensitive targets, and IoU-style localization losses are unstable for few-pixel boxes. We propose PromptScaleDINO as a lightweight adaptation framework for Grounding DINO. It introduces an Anchor Prompt Bank (APB) to enrich the text input while keeping supervision connected to a stable anchor phrase, a Scale-Aware Query Refinement Network (SQRN) to refine selected queries according to predicted box scale and semantic confidence, and a geometry-aware localization objective that combines Normalized Wasserstein Distance (NWD) with Generalized Intersection over Union (GIoU). Under a unified detection protocol, PromptScaleDINO achieves its largest gains on the challenging real-scene IRSTD-1k benchmark while maintaining near-ceiling performance on SIRST and NUDT-SIRST. On IRSTD-1k, it reaches 88.32% F1 and 87.40% mAP@0.5, improving the baseline by 2.56 and 4.00 percentage points, respectively. These gains mainly reflect improved weak-target recovery at comparable precision; accordingly, we position the framework as a target-sensitivity and localization adaptation rather than an explicit false-alarm suppression method.

Infrared stripe noise, mainly caused by detector nonuniformity and readout inconsistencies, is a common structured degradation in infrared imaging systems, exhibiting pronounced directional bias, strong column-wise persistence, and distinctive frequency-domain characteristics. These properties make generic restoration networks prone to a trade-off between stripe suppression and detail preservation: conventional two-dimensional attention often allocates modeling capacity to stripe-irrelevant spatial dependencies, early downsampling may entangle directional stripe components with scene structures, and skip connections in U-shaped architectures can reintroduce shallow residual stripe features into the decoder. Residual stripe artifacts and restoration-induced structural distortions can further impair downstream infrared image analysis, particularly small-target detection. To address these issues, we propose a Spatio-Frequency Synergistic Progressive Restoration Network (SFPRNet) for infrared image destriping. SFPRNet progressively exploits the column-wise statistical characteristics of stripe noise, directional frequency information during early scale transformation, and selective cross-level feature refinement to enhance stripe discrimination and suppression while preserving structural details. Extensive experiments on synthetic and real infrared images demonstrate that the proposed SFPRNet achieves superior or competitive performance across most datasets and degradation settings, providing a favorable balance between destriping quality and detail preservation. Furthermore, downstream evaluation with multiple infrared small-target detectors demonstrates that SFPRNet improves subsequent small-target detection performance.