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SIDE-A Unified Framework for Simultaneously Dehazing and Enhancement of Nighttime Hazy Images
2020
Sensors
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In this paper, we propose a novel unified nighttime hazy image enhancement framework to address the problems of both haze removal and illumination enhancement simultaneously. ...
Firstly, we decompose the observed hazy image into a halo layer and a scene layer to remove the influence of multiple scattering. ...
In this paper, we address both haze removal and illumination enhancement for nighttime hazy images in a unified framework. ...
doi:10.3390/s20185300
pmid:32947978
pmcid:PMC7570461
fatcat:l4qdyxi4jjfv7ju7g7stcfqo3u
Improved partial differential equation and fast approximation algorithm for hazy/underwater/dust storm image enhancement
2019
Advances In Image and Video Processing
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Thus, it followed that by reversing hazy images, illumination correction algorithms could be applied to process hazy images with some modifications. ...
Furthermore, the proposed algorithm is utilized for underwater and dust storm image enhancement with the incorporation of a modified global contrast enhancement algorithm. ...
The outline of the paper is as follows; the second section presents a brief overview and background relating to illumination and reflectance estimation. ...
doi:10.14738/aivp.73.6694
fatcat:j6him27darhyziw2gzq5y55e34
Sky detection and log illumination refinement for PDE-based hazy image contrast enhancement
[article]
2018
arXiv
pre-print
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Additionally, a proposed alternative method utilizes a function for log illumination refinement to improve de-hazing results while avoiding over-enhancement of sky or homogeneous regions. ...
This report presents the results of a sky detection technique used to improve the performance of a previously developed partial differential equation (PDE)-based hazy image enhancement algorithm. ...
Furthermore, several perform minimal enhancement of hazy images. A previous PDE-based formulation for hazy image enhancement was presented in previous work and yielded good results [22] . ...
arXiv:1712.09775v2
fatcat:kcml5dbxbfcl5d5iderdgjboea
A Novel Method for Night-Time Single Image Dehazing
2019
Journal of Computer and Communications
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Generally, there is the existence of numerous approaches towards haze removal which are mostly meant for hazy images under daytime environments. ...
The proposed scheme is a dark channel-based local image dehazing procedure that locally estimates the atmospheric intensity for each selected mask on a corrupted image independently and not the entire ...
The scheme continues with a mask combination operation for all the reconstructed masks of the night time haze image to produce an enhanced image under nighttime environment. ...
doi:10.4236/jcc.2019.711006
fatcat:xp7qe7ssdva63eg6xirlgf5ivi
CNN-Enabled Visibility Enhancement Framework for Vessel Detection under Haze Environment
2021
Journal of Advanced Transportation
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To avoid the failure of vessel detection caused by fog, it is necessary to preprocess the collected hazy images for recovering vital information. ...
Meanwhile, a hybrid loss function is designed for monitoring the multiscale output of C-FEM and the final result of F-FFM simultaneously. ...
CNN-Enabled Visibility Enhancement Framework In this section, a CNN-enabled visibility enhancement framework is proposed to process hazy maritime images shown in Figure 3 . is framework consists of two ...
doi:10.1155/2021/5598390
doaj:3ca0859272914c2a8c0c7eedcfe209dd
fatcat:dbri3kmm4ng6jhjkg3g4c5zhlq
An Effective Framework for Enhancement of Hazed and Low-Illuminated Images
2022
International Journal for Research in Applied Science and Engineering Technology
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Therefore, firstly, we will give a hazy low-illuminated image having low light as input and then apply a technique to clarify the visibility of the input image. ...
In this paper, we propose a new frame- work for presenting night-time hazy imaging, which works on haze removal and low-illumination correction algorithm taking into consideration both the non-uni- form ...
CONCLUSION In this paper the problem related to Low Illumination Image Enhancement for finding the visibility of long-distance objects in hazy low light images is a great challenge for us, and the presence ...
doi:10.22214/ijraset.2022.40382
fatcat:qmrjejv2ynezrgr6cydowiuc2i
L^2UWE: A Framework for the Efficient Enhancement of Low-Light Underwater Images Using Local Contrast and Multi-Scale Fusion
[article]
2020
arXiv
pre-print
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We create two distinct models and generate two enhanced images from them: one that highlights finer details, the other focused on darkness removal. ...
We present a novel single-image low-light underwater image enhancer, L^2UWE, that builds on our observation that an efficient model of atmospheric lighting can be derived from local contrast information ...
With L 2 UWE we propose a better image enhancement mechanism by deriving more realistic models for underwater illumination. ...
arXiv:2005.13736v2
fatcat:x6guu7endffifm5uke2rtt4qvi
L2UWE: A Framework for the Efficient Enhancement of Low-Light Underwater Images Using Local Contrast and Multi-Scale Fusion
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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We create two distinct models and generate two enhanced images from them: one that highlights finer details, the other focused on darkness removal. ...
We present a novel singleimage low-light underwater image enhancer, L 2 UWE, that builds on our observation that an efficient model of atmospheric lighting can be derived from local contrast information ...
With L 2 UWE we propose a better image enhancement mechanism by deriving more realistic models for underwater illumination. ...
doi:10.1109/cvprw50498.2020.00277
dblp:conf/cvpr/MarquesA20
fatcat:jhi4fq2wpbectcrnyjtnu5cvge
Uneven Image Dehazing by Heterogeneous Twin Network
2020
IEEE Access
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Removing the haze in an image is a huge challenge due to the difficulty of accurate hazy image modeling. ...
Consequently, the inhomogeneous haze is removed by the symmetric U-shape network with encoder-decoder structure, meanwhile, the other enhancement network extracts the high-frequency feature from the hazy ...
The enhancing framework can be formulated as: f r (I (x)) = I 1 (x) (12) where I (x) is the hazy image, f r is the enhancing framework, and I 1 (x) is the high-frequency information.
3) FUSION OF THE ...
doi:10.1109/access.2020.3003784
fatcat:3ew5lipp6bguzpev2zv6mtfy6y
Single Image Dehazing and Edge Preservation Based on the Dark Channel Probability-Weighted Moments
2019
Mathematical Problems in Engineering
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Dark channel prior (DCP) and probability-weighted moments (PWMs) are applied on each channel of an image to suppress the hazy regions and enhance the true edges. ...
We have proposed a method in this article that performs well as compared to state-of-the-art image dehazing techniques in various conditions which include illumination changes, contrast variation, and ...
Tarel and Hautière [11] presented a technique for image dehazing based on enhanced visibility in real-time processing and less complex for both color and grey images. is algorithm is based on maximum ...
doi:10.1155/2019/9721503
fatcat:yi6el75tsvatnav22b6a47w7va
Night Time Haze and Glow Removal using Deep Dilated Convolutional Network
[article]
2019
arXiv
pre-print
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For our recurrent network training, the hazy images and the corresponding transmission maps are synthesized from the NYU depth datasets and consequently restored a high-quality haze-free image. ...
To address these effects we introduce a deep learning based DeGlow-DeHaze iterative architecture which accounts for varying color illumination and glows. ...
However, these methods may not be applicable for night hazy images due to non-uniform and multicolored illumination during nighttime. ...
arXiv:1902.00855v1
fatcat:pnqb7pvttfb6pav2n64pzst6ti
Towards a General Model for Reflection Recovery and Single Image Enhancement
2020
IET Image Processing
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In this study, the authors propose a simple but effective method for estimating the reflection and enhancing the image contrast based on a general imaging model. ...
The haze imaging model is widely used in contrast enhancement in daylight condition with haze, while the retinex model is universal for low-light conditions. ...
[26] used a variational framework to separate illumination and reflection first. Then, a total variation model for retinex [27] was introduced. Wang et al. ...
doi:10.1049/iet-ipr.2019.1175
fatcat:6lbgsvvztffm5mpdlnxgttnid4
Multi-purpose Oriented Real-world Underwater Image Enhancement
2020
IEEE Access
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Herein, a novel multi-purpose oriented approach for real-world underwater image enhancement is proposed. ...
Subsequently, compensation of the brightness is carried out on the illumination layer, while color correction and contrast enhancement are implemented on the reflectance layer through a multi-scale processing ...
Herein, the ambition of this paper is in an attempt to develop a new framework for the restoration of complex real-world underwater images. ...
doi:10.1109/access.2020.3002883
fatcat:5yovnqghibf3zosv3qoipmqoe4
Image Dehazing Based on Local and Non-Local Features
2022
Fractal and Fractional
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Image dehazing is a traditional task, yet it still presents arduous problems, especially in the removal of haze from the texture and edge information of an image. ...
Extensive experiments display the effectiveness of the proposed method, which surpasses the state-of-the-art methods for most synthetic and real-world images, quantitatively and qualitatively. ...
For indoor images, all image dehazing methods have a better effect in uniform illumination. ...
doi:10.3390/fractalfract6050262
fatcat:u2d5gqij7zev7dqxhifvo4witi
Prior‐guided multiscale network for single‐image dehazing
2021
IET Image Processing
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Single-image dehazing is an important problem because it is a key prerequisite for most high-level computer vision tasks. ...
Traditional prior-based methods adopt priors generated from clear images to restrain the atmospheric scattering model and then recover haze-free images. ...
For example, enhanced pix2pix dehazing network (EPDN) [22] improves the dehazed images based on adversarial training between a pix2pixHD generator [23] and a multiscale discriminator. Zhou et al. ...
doi:10.1049/ipr2.12333
fatcat:bnuraz27mffxbnzdcwdn2d632q
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