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

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

Diffusion models have gained significant popularity for image-to-image translation tasks.
Previous efforts applying diffusion models to image super-resolution have demonstrated that …

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 …

Generative AI models have revolutionized various fields by enabling the creation of realistic
and diverse data samples. Among these models, diffusion models have emerged as a …

Buildings and roads are the two most basic man-made environments that carry and
interconnect human society. Building and road information has important application value …

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

Global and local modeling is essential for image super-resolution tasks. However, current
efforts often lack explicit consideration of the cross-scale knowledge in large-scale earth …

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 …

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 …