This paper reviews the current state of the art in artificial intelligence (AI) technologies and
applications in the context of the creative industries. A brief background of AI, and …

Deep convolutional networks–based super-resolution is a fast-growing field with numerous
practical applications. In this exposition, we extensively compare more than 30 state-of-the …

Previous works have shown that increasing the window size for Transformer-based image
super-resolution models (eg, SwinIR) can significantly improve the model performance but …

For any video codecs, the coding efficiency highly relies on whether the current signal to be
encoded can find the relevant contexts from the previous reconstructed signals. Traditional …

Recently, transformer-based methods have demonstrated impressive results in various
vision tasks, including image super-resolution (SR), by exploiting the self-attention (SA) for …

Video restoration aims to restore high-quality frames from low-quality frames. Different from
single image restoration, video restoration generally requires to utilize temporal information …

Change detection is an important tool for long-term Earth observation missions. It takes bi-
temporal images as input and predicts “where” the change has occurred. Different from other …

Image deblurring is a classic problem in low-level computer vision with the aim to recover a
sharp image from a blurred input image. Advances in deep learning have led to significant …

Video restoration aims at restoring multiple high-quality frames from multiple low-quality
frames. Existing video restoration methods generally fall into two extreme cases, ie, they …

A recurrent structure is a popular framework choice for the task of video super-resolution.
The state-of-the-art method BasicVSR adopts bidirectional propagation with feature …