Super-resolution (SR) technique reconstructs a higher-resolution image or sequence from
the observed LR images. As SR has been developed for more than three decades, both …

In recent years, advancements in machine learning (ML) techniques, in particular, deep
learning (DL) methods have gained a lot of momentum in solving inverse imaging problems …

Single image super-resolution (SISR) has played an important role in the field of image
processing. Recent generative adversarial networks (GANs) can achieve excellent results …

We present a fully automatic algorithm to identify fluid-filled regions and seven retinal layers
on spectral domain optical coherence tomography images of eyes with diabetic macular …

For the classification of hyperspectral images (HSIs), this paper presents a novel framework
to effectively utilize the spectral-spatial information of superpixels via multiple kernels, which …

Age-related macular degeneration is the leading cause of irreversible visual dysfunction in
individuals over 65 in Western Society. Patients with AMD are classified as having early …

Sparse representation has been demonstrated to be a powerful tool in classification of
hyperspectral images (HSIs). The spatial context of an HSI can be exploited by first defining …

A novel superpixel-based discriminative sparse model (SBDSM) for spectral-spatial
classification of hyperspectral images (HSIs) is proposed. Here, a superpixel in a HSI is …

Optical coherence tomography (OCT) is an efficient noninvasive bioimaging technique that
can measure retinal tissue. Considering the changes in the acquisition environment during …

We developed a deep learning algorithm for the automatic segmentation and quantification
of intraretinal cystoid fluid (IRC) in spectral domain optical coherence tomography (SD-OCT) …