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Remote sensing data fusion with generative adversarial networks: State-of-the-art methods and future research directions
In the past decades, remote sensing (RS) data fusion has always been an active research
community. A large number of algorithms and models have been developed. Generative …
community. A large number of algorithms and models have been developed. Generative …
A review on Single Image Super Resolution techniques using generative adversarial network
Abstract Single Image Super Resolution (SISR) is a process to obtain a high pixel density
and refined details from a low resolution (LR) image to get upscaled and sharper high …
and refined details from a low resolution (LR) image to get upscaled and sharper high …
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 …
Transweather: Transformer-based restoration of images degraded by adverse weather conditions
Removing adverse weather conditions like rain, fog, and snow from images is an important
problem in many applications. Most methods proposed in the literature have been designed …
problem in many applications. Most methods proposed in the literature have been designed …
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 …
RefineDNet: A weakly supervised refinement framework for single image dehazing
Haze-free images are the prerequisites of many vision systems and algorithms, and thus
single image dehazing is of paramount importance in computer vision. In this field, prior …
single image dehazing is of paramount importance in computer vision. In this field, prior …
Ultra-high-definition image dehazing via multi-guided bilateral learning
Convolutional neural networks (CNNs) have achieved significant success in the single
image dehazing task. Unfortunately, most existing deep dehazing models have high …
image dehazing task. Unfortunately, most existing deep dehazing models have high …
Enlightengan: Deep light enhancement without paired supervision
Deep learning-based methods have achieved remarkable success in image restoration and
enhancement, but are they still competitive when there is a lack of paired training data? As …
enhancement, but are they still competitive when there is a lack of paired training data? As …
Domain adaptation for image dehazing
Image dehazing using learning-based methods has achieved state-of-the-art performance in
recent years. However, most existing methods train a dehazing model on synthetic hazy …
recent years. However, most existing methods train a dehazing model on synthetic hazy …