From degrade to upgrade: Learning a self-supervised degradation guided adaptive network for blind remote sensing image super-resolution
Over the past few years, single image super-resolution (SR) has become a hotspot in the
remote sensing area, and numerous methods have made remarkable progress in this …
remote sensing area, and numerous methods have made remarkable progress in this …
Cycmunet+: Cycle-projected mutual learning for spatial-temporal video super-resolution
Spatial-Temporal Video Super-Resolution (ST-VSR) aims to generate high-quality videos
with higher resolution (HR) and higher frame rate (HFR). Quite intuitively, pioneering two …
with higher resolution (HR) and higher frame rate (HFR). Quite intuitively, pioneering two …
Magic ELF: Image deraining meets association learning and transformer
Convolutional neural network (CNN) and Transformer have achieved great success in
multimedia applications. However, little effort has been made to effectively and efficiently …
multimedia applications. However, little effort has been made to effectively and efficiently …
Multi-frequency representation enhancement with privilege information for video super-resolution
CNN's limited receptive field restricts its ability to capture long-range spatial-temporal
dependencies, leading to unsatisfactory performance in video super-resolution. To tackle …
dependencies, leading to unsatisfactory performance in video super-resolution. To tackle …
Refined semantic enhancement towards frequency diffusion for video captioning
Video captioning aims to generate natural language sentences that describe the given video
accurately. Existing methods obtain favorable generation by exploring richer visual …
accurately. Existing methods obtain favorable generation by exploring richer visual …
Generating a long-term (2003− 2020) hourly 0.25° global PM2. 5 dataset via spatiotemporal downscaling of CAMS with deep learning (DeepCAMS)
Generating a long-term high-spatiotemporal resolution global PM 2.5 dataset is of great
significance for environmental management to mitigate the air pollution concerns worldwide …
significance for environmental management to mitigate the air pollution concerns worldwide …
Store and fetch immediately: Everything is all you need for space-time video super-resolution
Existing space-time video super-resolution (ST-VSR) methods fail to achieve high-quality
reconstruction since they fail to fully explore the spatial-temporal correlations, long-range …
reconstruction since they fail to fully explore the spatial-temporal correlations, long-range …
Scratch Each Other's Back: Incomplete Multi-Modal Brain Tumor Segmentation via Category Aware Group Self-Support Learning
Abstract Although Magnetic Resonance Imaging (MRI) is very helpful for brain tumor
segmentation and discovery, it often lacks some modalities in clinical practice. As a result …
segmentation and discovery, it often lacks some modalities in clinical practice. As a result …
Toward real-world light field super-resolution
Deep learning has opened up new possibilities for light field super-resolution (SR), but
existing methods trained on synthetic datasets with simple degradations (eg, bicubic …
existing methods trained on synthetic datasets with simple degradations (eg, bicubic …
Progressive spatial-temporal collaborative network for video frame interpolation
Most video frame interpolation (VFI) algorithms infer the intermediate frame with the help of
adjacent frames through the cascaded motion estimation and content refinement. However …
adjacent frames through the cascaded motion estimation and content refinement. However …