Super resolution problems are widely discussed in medical imaging. Spatial resolution of
medical images are not sufficient due to the constraints such as image acquisition time, low …

Image inpainting has been widely exploited in the field of computer vision and image
processing. The main purpose of image inpainting is to produce visually plausible structure …

Numerous task-specific variants of conditional generative adversarial networks have been
developed for image completion. Yet, a serious limitation remains that all existing algorithms …

Existing deep learning based image inpainting methods use a standard convolutional
network over the corrupted image, using convolutional filter responses conditioned on both …

High-quality image inpainting requires filling missing regions in a damaged image with
plausible content. Existing works either fill the regions by copying high-resolution patches or …

Most image completion methods produce only one result for each masked input, although
there may be many reasonable possibilities. In this paper, we present an approach for …

Deep convolutional networks have become a popular tool for image generation and
restoration. Generally, their excellent performance is imputed to their ability to learn realistic …

Recent years have witnessed the unprecedented success of deep convolutional neural
networks (CNNs) in single image super-resolution (SISR). However, existing CNN-based …

We present a novel approach for image completion that results in images that are both
locally and globally consistent. With a fully-convolutional neural network, we can complete …

Camera sensors often fail to capture clear images or videos in a poorly lit environment. In
this paper, we propose a trainable hybrid network to enhance the visibility of such degraded …