Most existing studies performed video denoising by using multiple adjacent noisy frames to
recover one clean frame; however, despite achieving relatively good quality for each …

We propose a self-supervised approach for training multi-frame video denoising networks.
These networks predict frame t from a window of frames around t. Our self-supervised …

While significant progress has been made in deep video denoising, it remains very
challenging for exploiting historical and future frames. Bidirectional recurrent networks …

Challenging motion, which tends to cause artifacts, is a key problem in the video denoising
task. Recent video denoising methods have attempted to address this problem. However …

The advancement of imaging devices and countless images generated everyday pose an
increasingly high demand on image denoising, which still remains a challenging task in …

Learning-based video denoisers have attained state-of-the-art (SOTA) performances on
public evaluation benchmarks. Nevertheless, they typically encounter significant …

Due to the limited network bandwidth and low-quality end-user devices, the images in
distance education videos are transferred in low resolution. It results in low clarity, which …

In the process of sports movements, relevant actions occur at a fast pace. In the field of
artificial intelligence, recognizing such action features poses significant challenges, thereby …

New words detection, as basic research in natural language processing, has gained
extensive concern from academic and business communities. When the existing Chinese …

Seismic signals are inevitably disturbed by random noise in the acquisition process, which
greatly degrades seismic data. In order to improve the quality of seismic data, we propose a …