Deep-learning (DL) methods require immense amounts of labeled data to provide
reasonable results. In computer vision applications, and more specifically in despeckling …

Spatial resolution plays a crucial role in quantitative assessment of various structures in
brain MRI. Super resolution (SR) as a post-processing tool holds promise for restoring the …

传统的合成孔径雷达图像去噪算法在细节保存能力和运行时间上存在局限性,
而深度学习方法具有独特优势. 通过对国内外有关文献的归纳和总结, 分析了基于深度学习的 …

Abstract When training Artificial Intelligence and Deep Learning models, especially by using
Supervised Learning techniques, a labeled dataset is required to have an input with data …

In the pursuit of robust eye gaze estimation, traditional approaches often grapple with the
limitations of either spatial granularity or model interpretability. This paper introduces a dual …

In the field of computer vision, Deep Learning (DL) emerges as an extremely useful and
powerful tool for image processing. When training DL models with the supervised learning …

Despeckling SAR images constitute an important step when utilizing these types of images
to ensure the quality of applications reliant upon them. After applying any despeckling filter …

Supervised learning requires labeled data to train models and then make predictions from
new input data. Deep Learning (DL) methods require immense amounts of training data and …