September 2026

Deep Neural Networks (DNNs) remain vulnerable to adversarial perturbations, raising significant concerns in image processing applications, particularly in high-stakes domains such as medical imaging a

Facial emotion recognition (FER) has traditionally focused on a limited set of basic expressions, often failing to capture the complexity, subtlety, and cultural variability of real-world human emotio

In semi-supervised object detection (SSOD), due to the limited availability of labeled data, the quality and quantity of pseudo labels generated from unlabeled images are crucial for model training. O

Recently, Mamba-based super-resolution (SR) methods have demonstrated the ability to capture global receptive fields with linear complexity, addressing the quadratic computational cost of Transformer-

Despite the rapid progress of restoration-aware compression, existing studies still face challenges in low-bitrate restoration scenarios. Real-world image acquisition is inevitably affected by degrada

Semi-supervised object detection (SSOD) in aerial imagery is significantly hampered by extreme object scale variations, primarily due to diverse spatial resolutions inherent in aerial data, measurable

In hyperspectral image classification, existing cross-domain few-shot learning (CDFSL) approaches primarily focus on feature-level adaptation yet often neglect distribution-level shifts, which restric

Examining thyroid fine needle aspiration (FNA) can grade cancer risks, derive prognostic information, and guide follow-up care or surgery decision-making. However, thyroid cytology’s diagnostic cues a

Diffusion models have been used extensively for hyperspectral and multispectral image fusion; however, their intrinsic hallucination phenomenon frequently results in a loss of high-frequency details i

Hyperspectral change detection (HCD) has found numerous key applications, such as land cover monitoring. The majority of benchmark HCD algorithms are semi-supervised methods, and some of them can even