3 accepted papers
Image dehazing continues to be one of the most challenging inverse problems. Deep learning methods have emerged to complement traditional model-based methods and have helped define a new state of the art in achievable dehazed image quality. However, most deep learning-based methods usually design a…
Single image super-resolution (SR) via deep learning has recently gained significant attention in the literature. Convolutional neural networks (CNNs) are typically learned to represent the mapping between low-resolution (LR) and high-resolution (HR) images/patches with the help of training examples…
Deep learning methods, in particular trained Convolutional Neural Networks (CNNs) have recently been shown to produce compelling state-of-the-art results for single image Super-Resolution (SR). Invariably, a CNN is learned to map the low resolution (LR) image to its corresponding high resolution (HR…