Recent advancements in signal processing (SP) and machine learning, coupled with
electronic medical record kee** in hospitals and the availability of extensive sets of …

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 …

Benefiting from the development of deep convolutional neural networks, CNN-based single-
image super-resolution methods have achieved remarkable reconstruction results …

Optical coherence tomography (OCT) has become a very promising diagnostic method in
clinical practice, especially for ophthalmic diseases. However, speckle noise and low …

Images with high resolution are desirable in many applications such as medical imaging,
video surveillance, astronomy etc. In medical imaging, images are obtained for medical …

Single image super-resolution (SISR) is of great importance as a low-level computer vision
task. The fast development of Generative Adversarial Network (GAN) based deep learning …

In this paper, a novel denoising approach based on optimal trilateral filtering using Grey
Wolf Optimization (GWO) is proposed. At first, a database of noisy images are generated by …

In this paper, a single-computed tomography (CT) image super-resolution (SR)
reconstruction scheme is proposed. This SR reconstruction scheme is based on sparse …

The practical application of Computed Tomography (CT) faces the dilemma between higher
image resolution and less X-ray exposure for patients, motivating the research on CT super …

Super resolution reconstruction can be used to recover a high resolution image from a low
resolution image and is particularly beneficial for clinically significant medical images in …