As the computing power of modern hardware is increasing strongly, pre-trained deep
learning models (eg, BERT, GPT-3) learned on large-scale datasets have shown their …

We propose a new end-to-end single image dehazing method, called Densely Connected
Pyramid Dehazing Network (DCPDN), which can jointly learn the transmission map …

We present DeblurGAN, an end-to-end learned method for motion deblurring. The learning
is based on a conditional GAN and the content loss. DeblurGAN achieves state-of-the art …

In this paper, we present an extensive review on deep learning methods for image
restoration tasks. Deep learning techniques, led by convolutional neural networks, have …

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 dehazing is a challenging under-constrained problem because of the
ambiguities of unknown scene radiance and transmission. Previous methods solve this …

Single image dehazing is a challenging task, for which the domain shift between synthetic
training data and real-world testing images usually leads to degradation of existing methods …

Recent years have witnessed significant progress in the area of single image dehazing,
thanks to the employment of deep neural networks and diverse datasets. Most of the existing …

Convolutional neural network (CNN)-based autonomous driving object detection algorithms
have excellent detection results on conventional datasets, but the detector performance can …

In this paper, we present a Haze-Aware Representation Distillation Generative Adversarial
Network (HardGAN) for single-image dehazing. Unlike previous studies that intend to model …