[HTML][HTML] A survey on GANs for computer vision: Recent research, analysis and taxonomy
In the last few years, there have been several revolutions in the field of deep learning,
mainly headlined by the large impact of Generative Adversarial Networks (GANs). GANs not …
mainly headlined by the large impact of Generative Adversarial Networks (GANs). GANs not …
Ntire 2020 challenge on nonhomogeneous dehazing
This paper reviews the NTIRE 2020 Challenge on NonHomogeneous Dehazing of images
(restoration of rich details in hazy image). We focus on the proposed solutions and their …
(restoration of rich details in hazy image). We focus on the proposed solutions and their …
All-in-one image restoration for unknown corruption
In this paper, we study a challenging problem in image restoration, namely, how to develop
an all-in-one method that could recover images from a variety of unknown corruption types …
an all-in-one method that could recover images from a variety of unknown corruption types …
Self-augmented unpaired image dehazing via density and depth decomposition
To overcome the overfitting issue of dehazing models trained on synthetic hazy-clean image
pairs, many recent methods attempted to improve models' generalization ability by training …
pairs, many recent methods attempted to improve models' generalization ability by training …
Contrastive learning for compact single image dehazing
Single image dehazing is a challenging ill-posed problem due to the severe information
degeneration. However, existing deep learning based dehazing methods only adopt clear …
degeneration. However, existing deep learning based dehazing methods only adopt clear …
Learning to enhance low-light image via zero-reference deep curve estimation
This paper presents a novel method, Zero-Reference Deep Curve Estimation (Zero-DCE),
which formulates light enhancement as a task of image-specific curve estimation with a deep …
which formulates light enhancement as a task of image-specific curve estimation with a deep …
Learning multiple adverse weather removal via two-stage knowledge learning and multi-contrastive regularization: Toward a unified model
In this paper, an ill-posed problem of multiple adverse weather removal is investigated. Our
goal is to train a model with a'unified'architecture and only one set of pretrained weights that …
goal is to train a model with a'unified'architecture and only one set of pretrained weights that …
Mb-taylorformer: Multi-branch efficient transformer expanded by taylor formula for image dehazing
In recent years, Transformer networks are beginning to replace pure convolutional neural
networks (CNNs) in the field of computer vision due to their global receptive field and …
networks (CNNs) in the field of computer vision due to their global receptive field and …
PSD: Principled synthetic-to-real dehazing guided by physical priors
Deep learning-based methods have achieved remarkable performance for image dehazing.
However, previous studies are mostly focused on training models with synthetic hazy …
However, previous studies are mostly focused on training models with synthetic hazy …
Multi-scale boosted dehazing network with dense feature fusion
In this paper, we propose a Multi-Scale Boosted Dehazing Network with Dense Feature
Fusion based on the U-Net architecture. The proposed method is designed based on two …
Fusion based on the U-Net architecture. The proposed method is designed based on two …