Super resolution problems are widely discussed in medical imaging. Spatial resolution of
medical images are not sufficient due to the constraints such as image acquisition time, low …

Ultrasonic testing (UT) is commonly used to inspect the geometric shape of internal damage
in composite materials and the test results need to be interpreted by trained experts. In this …

Recently, model-driven deep networks have achieved excellent detection performance on
infrared small targets in cluttered environments. However, its detection performance is …

Fluorescein angiography (FA) is a procedure used to image the vascular structure of the
retina and requires the insertion of an exogenous dye with potential adverse side effects …

Deep multi-task learning (DMTL) is an efficient machine learning technique that has been
widely utilized for facial expression recognition. However, current deep multi-task learning …

Deep learning techniques and deep networks have recently been extensively studied and
widely applied to single image super‐resolution (SR). Among them, channel attention has …

A longstanding challenge in Super-Resolution (SR) is how to efficiently enhance high-
frequency details in Low-Resolution (LR) images while maintaining semantic coherence …

Recently, the attention mechanism became the key issue for image super-resolution (SR)
because it has the ability to extract different features from the image according to the used …

Super-resolution is generally defined as a process to obtain high-resolution images form
inputs of low-resolution observations, which has attracted quantity of attention from …

Single image super-resolution is a well-established low-level vision task that aims to
reconstruct high-resolution images from low-resolution images. Methods based on …