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 …

The digital transformation in healthcare, propelled by the integration of deep learning
models and the Internet of Things (IoT), is creating unprecedented opportunities for …

It is essential to define more convincing and applicable classifiers for small datasets. In this
paper, a minimum reconstruction error-based K-nearest neighbors (K-NN) classifier is …

Deep learning has excelled in single-image super-resolution (SISR) applications, yet the
lack of interpretability in most deep learning-based SR networks hinders their applicability …

In microscopic vision, the sharpness of edge profile seriously affects the accuracy of
measurement. However, various factors in complex environments often blur the edge profile …

Abstract Medical Image Super-Resolution plays a pivotal role in enhancing diagnostic
accuracy. Transformer-based methods, such as Image Restoration Using Swin Transformer …

The high resolution of the image is very important for applications. Publicly available satellite
images generally have low resolutions. Since low resolution causes loss of information, the …

Medical images play a vital role in medical analysis by providing crucial information about
patients' pathological conditions. However, the quality of these images can be compromised …

Abstract Background and Objective Magnetic resonance imaging (MRI) can provide rich and
detailed high-contrast information of soft tissues, while the scanning of MRI is time …

Video super-resolution (VSR) aims to recover the high-resolution (HR) contents from the low-
resolution (LR) observations relying on compositing the spatial–temporal information in the …