Single-image super-resolution (SISR) is an important task in image processing, aiming to
achieve enhanced image resolution. With the development of deep learning, SISR based on …

Insufficient image spatial resolution is a serious limitation in many practical scenarios,
especially when acquiring images at a finer scale is infeasible or brings higher costs. This is …

High resolution Magnetic Resonance (MR) images are desired for accurate diagnostics. In
practice, image resolution is restricted by factors like hardware and processing constraints …

Transformer-based models have revolutionized the field of image super-resolution (SR) by
harnessing their inherent ability to capture complex contextual features. The overlapping …

With the introduction of deep learning, a significant amount of research has been conducted
in the field of computer vision in the past decade. In particular, research on object detection …

Recent years have witnessed the great success of image super-resolution based on deep
learning. However, it is hard to adapt a well-trained deep model for a specific image for …

Deep learning techniques have significantly improved image restoration tasks in recent
years. As a crucial component of deep learning, the loss function plays a key role in network …

Single image super‐resolution (SISR) has been revolutionized by convolutional neural
networks (CNN). However, existing SISR algorithms have feature extraction and adaptive …

The objective of image super-resolution is to reconstruct a high-resolution (HR) image with
the prior knowledge from one or several low-resolution (LR) images. However, in the real …

The traditional SRGAN image super-resolution reconstruction algorithm has a poor
reconstruction quality and does not consider the detailed information at different scales. To …