Diffusion models (DMs) have disrupted the image super-resolution (SR) field and further
closed the gap between image quality and human perceptual preferences. They are easy to …

Transformer-based method has demonstrated promising performance in image super-
resolution tasks, due to its long-range and global aggregation capability. However, the …

Convolutional neural networks (CNNs) and Transformers have made impressive progress in
the field of remote sensing change detection (CD). However, both architectures have …

Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …

Recent progress in remote sensing image (RSI) super-resolution (SR) has exhibited
remarkable performance using deep neural networks, eg, Convolutional Neural Networks …

As a newly emerging advance in deep generative models, diffusion models have achieved
state-of-the-art results in many fields, including computer vision, natural language …

A high-precision feature extraction model is crucial for change detection (CD). In the past,
many deep learning-based supervised CD methods learned to recognize change feature …

The adoption of Stereo Imaging technology within endoscopic procedures represents a
transformative advancement in medical imaging, providing surgeons with depth perception …

The current advanced hyperspectral super-resolution methods utilize convolutional neural
networks (CNNs) that are either deeper or wider. These networks are designed to acquire …

Remote sensing haze removal is a popular computational imaging technique that directly
obtains clear remote sensing data from hazy remote sensing images. Apart from prior-based …