A new fast method for foggy image enhancement

Abbaspour, Mohammad Javad; Yazdi, Mehran; Masnadi-Shirazi, Mohammad Ali

doi:10.1109/iraniancee.2016.7585823

Cited by 7 publications

(3 citation statements)

References 13 publications

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“…The method does not offer reliable results for some specific cases, one example being the images with a constant depth of the observed scene. A method based on a modified version of Koschmieder's model is presented in [28], where the atmospheric effect of fog is modeled first. Afterwards, the atmospheric veil is estimated using dark channel prior, and then an exponential transformation is applied to it to improve the accuracy of the estimation.…”

Section: Methods Based On Koschmieder Lawmentioning

confidence: 99%

Visibility Enhancement and Fog Detection: Solutions Presented in Recent Scientific Papers with Potential for Application to Mobile Systems

Miclea

Ungureanu

Sandru

et al. 2021

Sensors

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In mobile systems, fog, rain, snow, haze, and sun glare are natural phenomena that can be very dangerous for drivers. In addition to the visibility problem, the driver must face also the choice of speed while driving. The main effects of fog are a decrease in contrast and a fade of color. Rain and snow cause also high perturbation for the driver while glare caused by the sun or by other traffic participants can be very dangerous even for a short period. In the field of autonomous vehicles, visibility is of the utmost importance. To solve this problem, different researchers have approached and offered varied solutions and methods. It is useful to focus on what has been presented in the scientific literature over the past ten years relative to these concerns. This synthesis and technological evolution in the field of sensors, in the field of communications, in data processing, can be the basis of new possibilities for approaching the problems. This paper summarizes the methods and systems found and considered relevant, which estimate or even improve visibility in adverse weather conditions. Searching in the scientific literature, in the last few years, for the preoccupations of the researchers for avoiding the problems of the mobile systems caused by the environmental factors, we found that the fog phenomenon is the most dangerous. Our focus is on the fog phenomenon, and here, we present published research about methods based on image processing, optical power measurement, systems of sensors, etc.

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Section: Methods Based On Koschmieder Lawmentioning

confidence: 99%

Visibility Enhancement and Fog Detection: Solutions Presented in Recent Scientific Papers with Potential for Application to Mobile Systems

Miclea

Ungureanu

Sandru

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…To reduce computation time, they reduced image resolution and applied this algorithm in low resolution image and enhanced resolution at the end. Abbaspour [8] proposed a three‐step method based on atmospheric vision theory of Koschmieder. In the first step, he applied low‐pass filter to obtain a reference intensity value then in second step he applied logarithmic function to obtain target intensity level, then he calculated transmission map in the third step.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimisation of transmission map for improved image defogging

Tufail

Khurshid

Salman

2019

IET Image Processing

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Outdoor images taken in foggy weather are not suitable for automation due to low contrast. It is a challenging task to remove fog from images specially when the image contains large sky region. The authors propose dark channel-based single image defogging technique to estimate atmospheric light which represents the amount of luminance in a scene in the absence of fog. This atmospheric light is used to reconstruct fog-free image with a transmission map. Transmission map represents the effect of fog with respect to depth in image. In this study, they propose four transmission maps to reconstruct the images with different colour contrast. Proposed method adaptively selects a transmission map depending upon the fog density to reconstruct image with optimal colour contrast. The transmission map is refined by applying Laplacian filter followed by the guided filter. Previously, dark channel prior based methods were considered to be less effective for images with large sky region, but the proposed method reconstructs better result consistently for such images, independent of the density of the fog. Experimental results show that images reconstructed by proposed method are qualitatively better than the previously proposed methods.

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“…For image enhancement, the contrast of the foggy image can be restored using the dark channel prior (DCP) method and Gamma adjustment in the histogram of the affected images 3–6 . This method estimates the transmission ratio of atmospheric light and then adjusts the histogram of images accordingly using the Gamma transform.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligent based smart system for safe mining during foggy weather

Kumari

Choudhary

Mishra

et al. 2021

Concurrency and Computation

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Opencast mining operations at hilly areas are usually affected during foggy weather due to the inability of drivers to operate heavy earth‐moving machinery in low visibility conditions. This article deals with an intelligent vision enhancement system for continuing opencast mining operations during foggy weather. The system integrates hardware and software to provide multistage safety features that make it unique from existing systems. The system includes hardware like thermal cameras, high definition cameras, proximity radar, wireless devices, GNSS module, graphical processing unit, display unit, and so forth, and image processing software, namely real‐time image stitching, image enhancement, and object detection using convolutional neural networks. The integrated system and algorithms display a 180° panorama field view of the vehicle's front using real‐time video stitching. The front view after image processing, rear camera view, object detection through proximity radar, and real‐time location of the vehicles on a 3D geo‐tagged mine map by GNSS modules are displayed in four splitter windows on a touch screen fitted on the dashboard in front of the driver's seat. The driver can drive the vehicle by seeing the display screen during foggy weather. The output image of the developed image‐processing algorithm has less distortion, better quality, and better depth perception than existing methods. Overall, there are significant improvements in the persistence of the color elements by 39.65%, contrast by 4.62%, and the corresponding entropy by 7.11% concerning the similar existing methods. The final system has been successfully tested in an opencast mine.

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A new fast method for foggy image enhancement

Cited by 7 publications

References 13 publications

Visibility Enhancement and Fog Detection: Solutions Presented in Recent Scientific Papers with Potential for Application to Mobile Systems

Visibility Enhancement and Fog Detection: Solutions Presented in Recent Scientific Papers with Potential for Application to Mobile Systems

Optimisation of transmission map for improved image defogging

Artificial intelligent based smart system for safe mining during foggy weather

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