The main target of Single image super-resolution is to recover high-quality or high-resolution
image from degraded version of low-quality or low-resolution image. Recently, deep …

Super-resolution of face images, known as Face Hallucination (FH), has been excessively
studied in recent years. Modern FH methods use deep Convolution Neural Networks (CNN) …

In the field of computer vision, the application of hand-crafted as well as computer-learning-
based methods in the field of image quality assessment has yielded remarkable results …

Image super-resolution (SR) has been extensively investigated in recent years. However,
due to the absence of trustworthy and precise perceptual quality standards, it is challenging …

Image quality assessment (IQA) has a vital issue in image processing to measure the
perceptual quality of the image. This aims at the human visual system (HVS) by viewing for …

Recently, a surge of several research interests in deep learning has been sparked for image
super-resolution. Basically, a deep convolutional neural network is trained to identify the …

In this research, we propose a loss function for WDSR (Wide Activation Super Resolution)
model to enhance the edge of target object. In the design of our proposed loss function, in …

Super resolution problems are often discussed in medical imaging. The spatial resolution of
medical images is insufficient due to limitations such as image acquisition time, low radiation …