A Dual-Channel Dehaze-Net for Single Image Dehazing in Visual Internet of Things Using PYNQ-Z2 Board

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F24%3A50019622" target="_blank" >RIV/62690094:18450/24:50019622 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9934895" target="_blank" >https://ieeexplore.ieee.org/document/9934895</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TASE.2022.3217801" target="_blank" >10.1109/TASE.2022.3217801</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Dual-Channel Dehaze-Net for Single Image Dehazing in Visual Internet of Things Using PYNQ-Z2 Board

Popis výsledku v původním jazyce

A large number of emerging applications, such as autonomous navigation, space exploration, surveillance, military target detection, and remote sensing, use outdoor images to monitor various activities of interest. However, images acquired under unfavorable weather conditions usually suffer from atmospheric scattering due to environmental pollution causing color-shift and low-contrast images. Dehazing is an emerging research area in the computer vision domain that intends to restore the visibility of images by eliminating the latter types of degradation. Single image dehazing, on the other hand, is more challenging since it necessitates a precise assessment of atmospheric light and transmission map. This study aims to design a dual-channel deep neural network (DCD-Net) for estimating the transmission map, further utilized to compute atmospheric light. Finally, a dehazed image is generated using the obtained atmospheric light and the transmission map. The experimental results are compared qualitatively and quantitatively with eight existing dehazing approaches based on ten metrics on six publicly available standard datasets: Foggy Road Image DAtabase, HazeRD, REalistic Single Image DEhazing, NYU-Depth, O-HAZE, I-HAZE, a few natural hazy images, and underwater images. The DCD-Net outperforms conventional techniques, according to extensive studies. Moreover, a range of relative improvements of the proposed method over other approaches is calculated for better analysis of the results. A visual internet of things (VIoT) framework employing a PYNQ-Z2 board is presented in addition to the DCD-Net. It can be applied in real-time applications, particularly in the transportation and surveillance industries. The DCD-Net is suitable for image dehazing by virtue of its multilayered structure. The VIoT uses the DCD-Net for dehazing, while the PYNQ-Z2 board serves as the central processing unit. &lt;italic&gt;Note to Practitioners&lt;/italic&gt;—This paper is motivated by the problems occurring due to haze. Haze reduces the visibility of a scene, causing major concerns in transportation and surveillance. Existing approaches have attempted to address this issue, albeit the methods are limited. As a result, this study proposes a new dual-channel CNN model with two modules, where the first module calculates fine details of the image and the second module estimates the transmission map. Furthermore, both features are combined to produce a more reliable transmission map. The training process highly influences the resulting output of the network. Therefore, an algorithm explaining the training instructions for the practitioners is given in the appendix. The obtained transmission map is further used to estimate atmospheric light. The images are then dehazed using atmospheric light and transmission maps. In addition, we have designed a framework for image dehazing using VIoT with a PYNQ-Z2 board. Experimental results suggest that this approach gives expected results, yet, there is one limitation. This method requires a haze image and a corresponding transmission map, which is not always possible. Therefore, we will attempt to design a semi-supervised learning approach in the future. IEEE

Název v anglickém jazyce

A Dual-Channel Dehaze-Net for Single Image Dehazing in Visual Internet of Things Using PYNQ-Z2 Board

Popis výsledku anglicky

A large number of emerging applications, such as autonomous navigation, space exploration, surveillance, military target detection, and remote sensing, use outdoor images to monitor various activities of interest. However, images acquired under unfavorable weather conditions usually suffer from atmospheric scattering due to environmental pollution causing color-shift and low-contrast images. Dehazing is an emerging research area in the computer vision domain that intends to restore the visibility of images by eliminating the latter types of degradation. Single image dehazing, on the other hand, is more challenging since it necessitates a precise assessment of atmospheric light and transmission map. This study aims to design a dual-channel deep neural network (DCD-Net) for estimating the transmission map, further utilized to compute atmospheric light. Finally, a dehazed image is generated using the obtained atmospheric light and the transmission map. The experimental results are compared qualitatively and quantitatively with eight existing dehazing approaches based on ten metrics on six publicly available standard datasets: Foggy Road Image DAtabase, HazeRD, REalistic Single Image DEhazing, NYU-Depth, O-HAZE, I-HAZE, a few natural hazy images, and underwater images. The DCD-Net outperforms conventional techniques, according to extensive studies. Moreover, a range of relative improvements of the proposed method over other approaches is calculated for better analysis of the results. A visual internet of things (VIoT) framework employing a PYNQ-Z2 board is presented in addition to the DCD-Net. It can be applied in real-time applications, particularly in the transportation and surveillance industries. The DCD-Net is suitable for image dehazing by virtue of its multilayered structure. The VIoT uses the DCD-Net for dehazing, while the PYNQ-Z2 board serves as the central processing unit. &lt;italic&gt;Note to Practitioners&lt;/italic&gt;—This paper is motivated by the problems occurring due to haze. Haze reduces the visibility of a scene, causing major concerns in transportation and surveillance. Existing approaches have attempted to address this issue, albeit the methods are limited. As a result, this study proposes a new dual-channel CNN model with two modules, where the first module calculates fine details of the image and the second module estimates the transmission map. Furthermore, both features are combined to produce a more reliable transmission map. The training process highly influences the resulting output of the network. Therefore, an algorithm explaining the training instructions for the practitioners is given in the appendix. The obtained transmission map is further used to estimate atmospheric light. The images are then dehazed using atmospheric light and transmission maps. In addition, we have designed a framework for image dehazing using VIoT with a PYNQ-Z2 board. Experimental results suggest that this approach gives expected results, yet, there is one limitation. This method requires a haze image and a corresponding transmission map, which is not always possible. Therefore, we will attempt to design a semi-supervised learning approach in the future. IEEE

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Transactions on Automation Science and Engineering

ISSN

1545-5955

e-ISSN

1558-3783

Svazek periodika

21

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

305-319

Kód UT WoS článku

000926046800001

EID výsledku v databázi Scopus

2-s2.0-85141549576