Single image super-resolution (SISR), which aims to reconstruct a high-resolution (HR)
image from a low-resolution (LR) observation, has been an active research topic in the area …

In the recent decade, single-shot ultrafast optical imaging by active detection, called single-
shot active ultrafast optical imaging (SS-AUOI) here, has made great progress, eg, with a …

Whether conventional machine learning-based or current deep neural networks-based
single image super-resolution (SISR) methods, they are generally trained and validated on …

Magnetic resonance imaging (MRI) is an essential radiology technique in clinical diagnosis,
but its spatial resolution may not suffice to meet the growing need for precise diagnosis due …

Light Field (LF) image angular super-resolution aims to synthesize a high angular resolution
LF image from a low angular resolution one, and is drawing increased attention because of …

The challenge of image reconstruction from very-low-resolution images is made exceedingly
difficult by multiple degradation factors in practical applications. Traditional methods do not …

Single image super-resolution (SISR) is one of the contemporary research areas in the field
of image restoration that involves solving an ill-posed inverse equation. A rich profusion of …

La superresolució d'imatge única (SISR) és una tasca important en el processament
d'imatges, que té com a objectiu millorar la resolució dels sistemes d'imatges. Recentment …

In multimedia devices such as mobile phones, surveillance cameras, and web cameras,
image sensors have limited spatial resolution. As a result, the image captured from these …

Many advanced deep convolutional neural network (DCNN) methods have proven their
efficacy in reconstructing the texture of super-resolution images (SR) from low-resolution …