The lack of resolution of imaging systems has critically adverse impacts on the recognition
and performance of biometric systems, especially in the case of long range biometrics and …

Positron emission tomography (PET) is an essential technique in many clinical applications
such as tumor detection and brain disorder diagnosis. In order to obtain high-quality PET …

Recently, deep convolutional neural networks (CNNs) have been widely explored in single
image super-resolution (SISR) and obtained remarkable performance. However, most of the …

High-resolution (HR) magnetic resonance images (MRI) provide more detailed information
for clinical application. However, HR MRI is less available because of the longer scan time …

Automatic face recognition in the wild still suffers from low-quality, low resolution, noisy, and
occluded input images that can severely impact identification accuracy. In this paper, we …

Multi-atlas-based methods are commonly used for MR brain image labeling, which
alleviates the burdening and time-consuming task of manual labeling in neuroimaging …

Preliminary studies have shown the feasibility of deep learning (DL)-based super-resolution
(SR) technique for reconstructing thick-slice/gap diagnostic MR images into high-resolution …

Abstract The Random Forests algorithm had a substantial impact on medical image
computing over the last decade. This chapter presents basic algorithmic details, some …

In recent years, impressive advances have been made in single-image super-resolution.
Deep learning is behind much of this success. Deep (er) architecture design and external …

We propose a scale-invariant deep neural network model based on wavelets for single
image super-resolution (SISR). The wavelet approximation images and their corresponding …