We present a comprehensive study and evaluation of existing single-image dehazing
algorithms, using a new large-scale benchmark consisting of both synthetic and real-world …

Convolutional neural networks (CNNs) have shown dramatic improvements in single image
super-resolution (SISR) by using large-scale external samples. Despite their remarkable …

Recent research on super-resolution has progressed with the development of deep
convolutional neural networks (DCNN). In particular, residual learning techniques exhibit …

In this paper, we present a semi-supervised deep learning approach to accurately recover
high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the …

In this paper, we propose a very deep fully convolutional encoding-decoding framework for
image restoration such as denoising and super-resolution. The network is composed of …

Deep learning techniques have been successfully applied in many areas of computer vision,
including low-level image restoration problems. For image super-resolution, several models …

Existing enhancement methods are empirically expected to help the high-level end
computer vision task: however, that is observed to not always be the case in practice. We …

Single image super-resolution (SR) is an ill-posed problem, which tries to recover a high-
resolution image from its low-resolution observation. To regularize the solution of the …

We consider how image super-resolution (SR) can contribute to an object detection task in
low-resolution images. Intuitively, SR gives a positive impact on the object detection task …

Visual recognition research often assumes a sufficient resolution of the region of interest
(ROI). That is usually violated in practice, inspiring us to explore the Very Low Resolution …