Single image super-resolution (SISR), which aims to reconstruct a high-resolution (HR)
image from a low-resolution (LR) observation, has been an active research topic in the area …

In machine learning, the model is not as complicated as possible. Good generalization
ability means that the model not only performs well on the training data set, but also can …

Along with the massive growth of the Internet from the 1990s until now, various innovative
technologies have been created to bring users breathtaking experiences with more virtual …

Given a degraded input image, image restoration aims to recover the missing high-quality
image content. Numerous applications demand effective image restoration, eg …

Single image super-resolution (SISR) has witnessed great strides with the development of
deep learning. However, most existing studies focus on building more complex networks …

Blind face restoration is a highly ill-posed problem that often requires auxiliary guidance to
1) improve the map** from degraded inputs to desired outputs, or 2) complement high …

Both non-local (NL) operation and sparse representation are crucial for Single Image Super-
Resolution (SISR). In this paper, we investigate their combinations and propose a novel Non …

Deep neural networks provide unprecedented performance gains in many real-world
problems in signal and image processing. Despite these gains, the future development and …

While some studies have proven that Swin Transformer (Swin) with window self-attention
(WSA) is suitable for single image super-resolution (SR), the plain WSA ignores the broad …

With the goal of recovering high-quality image content from its degraded version, image
restoration enjoys numerous applications, such as in surveillance, computational …