Acceptability and Usability of Telemedicine for Older Veterans in a Pilot Sample: Mixed Methods Study.

Infrared Small Target Detection (ISTD) enables effective imaging under low-light conditions and has the capability to penetrate visual obstructions such as fog. It has attracted increasing attention across a range of critical applications, including traffic monitoring, early warning systems, and maritime surveillance [–]. Furthermore, by integrating underwater remote sensing technologies with infrared imaging, comprehensive ocean monitoring can be effectively realized, enabling enhanced perception and analysis of complex maritime environments [–]. Nevertheless, ISTD remains a highly challenging task. Due to long imaging distances, infrared targets typically appear extremely small—sometimes occupying only a single pixel—and inherently lack color and texture information. Furthermore, the significant attenuation of infrared radiation over distance leads to targets with very low contrast, which are often obscured by sensor noise and cluttered backgrounds, thereby hindering the reliable extraction of discriminative features. Traditional ISTD methods can be broadly categorized into three types: filtering-based methods [,], local contrast-based methods [–], and low-rank-based methods [–]. These approaches typically begin by generating a confidence map that suppresses background clutter while highlighting potential targets. An adaptive thresholding technique is then applied to this map to extract the targets. However, due to the absence of feature learning capabilities, these methods often struggle to generalize to complex real-world scenarios. With the rapid advancement of deep neural networks capable of automatically learning features from large-scale data encompassing complex scenes, CNN-based methods have demonstrated superior performance in ISTD compared to traditional approaches [–]. Liu et al. [] proposed a generic detection framework by designing a five-layer multi-layer perceptron (MLP) network specifically for infrared small target detection. Wang et al. [] decomposed the detection task into two sub-tasks, addressed by two adversarially trained models, achieving a balance between false alarms and missed detections. Dai et al. [] introduced a novel method that integrates discriminative networks with conventional model-driven techniques, leveraging local contrast priors to enhance infrared small target detection. However, the inherent limitation of CNNs in capturing long-range dependencies remains a significant bottleneck, restricting further improvements in small target detection. To mitigate this issue, various studies have employed lateral or skip connections that link feature maps of different resolutions and semantic levels, as seen in architectures such as U-Net and SharpMask []. Among these approaches, Feature Pyramid Networks (FPNs) [,] have emerged as a prominent solution for enhancing long-range dependency learning in CNNs and have been widely adopted in segmentation tasks. In recent years, spectral neural networks have gained increasing attention. By leveraging the spectral convolution theorem from Fourier theory—which indicates that modifications in the spectral domain can globally influence all input features []—the integration of frequency and spatial domains has proven to be an effective strategy for extending the receptive field [].