20 citations · 24 across the 6 of their papers we have counts for
6 papers
SlimDiffSR: Toward Lightweight and Efficient Remote Sensing Image Super-Resolution via Diffusion Model Distillation
Ce Wang, Zhenyu Hu, Wanjie Sun
Diffusion models have recently achieved remarkable performance in image super-resolution (SR), but their high computational cost limits practical deployment in remote sensing appli…
Controllable Reference Guided Diffusion with Local Global Fusion for Real World Remote Sensing Image Super Resolution
Ce Wang, Wanjie Sun
Super resolution techniques can enhance the spatial resolution of remote sensing images, enabling more efficient large scale earth observation applications. While single image SR m…
Map-Assisted Remote-Sensing Image Compression at Extremely Low Bitrates
Yixuan Ye, Ce Wang, Wanjie Sun +1
Remote-sensing (RS) image compression at extremely low bitrates has always been a challenging task in practical scenarios like edge device storage and narrow bandwidth transmission…
Timestep-Aware Diffusion Model for Extreme Image Rescaling
Ce Wang, Zhenyu Hu, Wanjie Sun +1
Image rescaling aims to learn the optimal low-resolution (LR) image that can be accurately reconstructed to its original high-resolution (HR) counterpart, providing an efficient im…
Semantic Guided Large Scale Factor Remote Sensing Image Super-resolution with Generative Diffusion Prior
Ce Wang, Wanjie Sun
Remote sensing images captured by different platforms exhibit significant disparities in spatial resolution. Large scale factor super-resolution (SR) algorithms are vital for maxim…
Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives
Jun Li, Junyu Chen, Yucheng Tang +3
Transformer, the latest technological advance of deep learning, has gained prevalence in natural language processing or computer vision. Since medical imaging bear some resemblance…