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 …

Image fusion is a well-recognized and a conventional field of image processing. Image
fusion provides an efficient way of enhancing and combining pixel-level data resulting in …

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

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 …

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

Recently, very deep convolutional neural networks (CNNs) have shown great power in
single image super-resolution (SISR) and achieved significant improvements against …

Recently, deep convolutional neural networks (CNNs) have been widely explored in single
image super-resolution (SISR) and obtained remarkable performance. However, most of the …

Recent advances in image super-resolution (SR) explored the power of deep learning to
achieve a better reconstruction performance. However, the feedback mechanism, which …

To improve the issue of low-frequency and high-frequency components from feature maps
being treated equally in existing image super-resolution reconstruction methods, the paper …

In this paper, we propose dense feature fusion (DFF) for image super-resolution (SR). As the
same content in different natural images often have various scales and angles of view …