Super resolution (SR) is an eminent system in the field of computer vison and image
processing to improve the visual perception of the poor-quality images. The key objective of …

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 …

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

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 …

Image restoration is a long-standing low-level vision problem that aims to restore high-
quality images from low-quality images (eg, downscaled, noisy and compressed images) …

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 …

Existing dehazing approaches struggle to process real-world hazy images owing to the lack
of paired real data and robust priors. In this work, we present a new paradigm for real image …

Recent works on plug-and-play image restoration have shown that a denoiser can implicitly
serve as the image prior for model-based methods to solve many inverse problems. Such a …

Deep convolutional neural networks can effectively improve the performance of single-
image super-resolution reconstruction. Deep networks tend to achieve better performance …

Informative features play a crucial role in the single image super-resolution task. Channel
attention has been demonstrated to be effective for preserving information-rich features in …