The paper presents MM‑IQA, a lightweight no‑reference image quality assessment framework designed for UAV imaging. It fuses interpretable metrics—blur, edge structure, low‑resolution artifacts, exposure imbalance, noise, haze, and frequency content—to output a single quality score between 0 and 100. Evaluated on five benchmark datasets, MM‑IQA achieved SRCC values from 0.647 to 0.830 and runs in about 1.97 s per image with modest memory usage.
Low-light image enhancement algorithms (LIEAs) aim to improve the visibility of images captured under poor illumination. However, the enhancement process often introduces artifacts such as noise amplification, color shift, structural damage, and over-exposure, which degrade the perceptual quality of the enhanced images.
arXiv:2609.05762v1 Announce Type: cross Abstract: UAV image collections contain spatially and temporally related frames, yet semantic-segmentation benchmarks commonly split them at image level. Such...
arXiv:2608.23136v1 Announce Type: new Abstract: Poor illumination obscures small, low-contrast defects in UAV bridge imagery, reducing the reliability and operational flexibility of automated inspect...
arXiv:2512. 18046v2 Announce Type: replace Abstract: Unmanned Aerial Vehicles, commonly known as, drones pose increasing risks in civilian and defense settings, demanding accurate and real-time drone detection systems.
arXiv:2606. 16082v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have been increasingly adopted for Image Quality Assessment (IQA).
arXiv:2609.14495v1 Announce Type: new Abstract: Image colorization is an inherently ill-posed task, since a single grayscale image may correspond to multiple plausible colorized results. Consequently...
Poor illumination obscures small, low-contrast defects in UAV bridge imagery, reducing the reliability and operational flexibility of automated inspection. This paper investigates whether degradation-...
The paper presents a multi‑campaign UAV thermal image dataset for inert ordnance screening, comprising 5,855 labeled image pairs collected in Tennessee across diverse terrains and seasons. The authors trained YOLOV11l and RT‑DETR‑R50 models on 33 m and 15 m altitude data, achieving automated candidate detection, and provided practical guidelines for future humanitarian mine action surveys. The dataset and models aim to aid screening and prioritization for follow‑up technical surveys or EOD assessment, not to replace clearance operations.
arXiv:2610.00141v1 Announce Type: new Abstract: Vision-based anti-UAV systems must function in poor visibility, yet most benchmarks use only clear-sky footage, and previous robustness studies treat a...
The paper investigates how raw or minimally processed satellite imagery affects onboard AI object detection for space missions. By systematically degrading Very High Resolution Maxar images in terms of Signal‑to‑Noise Ratio, Modulation Transfer Function, and Ground Sampling Distance, the authors evaluate three lightweight detectors—YOLOv5s, YOLOX‑S, and NanoDet—on the resulting data. Results show that image quality impacts detection performance in a degradation‑specific way, with GSD consistently shifting performance, while MTF and SNR effects vary by model and resolution; severe blur‑plus‑noise combinations cause the greatest losses.
arXiv:2601. 21738v2 Announce Type: replace-cross Abstract: Evaluation of Image Quality Assessment (IQA) models has long been dominated by global correlation metrics, such as Pearson Linear Correlation Coefficient (PLCC) and Spearman Rank-Order Correlation Coefficient (SRCC).
arXiv:2609.27959v1 Announce Type: cross Abstract: We investigate a local spectral-complexity representation for perceptual image quality assessment (IQA) based on Shannon entropy of singular values c...