Atmospheric light estimation in hazy images based on color-plane model

Zhu, Ming‐Zhu; He, Bingwei; Zhang, Liwei

doi:10.1016/j.cviu.2017.09.005

Cited by 11 publications

(5 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on Middleton's model [1], a number of methods have been proposed to remove haze from single images [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12]. The key to their success is based on the above optical model in Eq.…”

Section: I(x) = J(x)t(x) + A[1 − T(x)] = Aρ(x)t(x) + A[1 − T(x)]mentioning

confidence: 99%

Nighttime image dehazing based on Retinex and dark channel prior using Taylor series expansion

Qunfang

Jia

et al. 2021

Computer Vision and Image Understanding

View full text Add to dashboard Cite

Section: I(x) = J(x)t(x) + A[1 − T(x)] = Aρ(x)t(x) + A[1 − T(x)]mentioning

confidence: 99%

Nighttime image dehazing based on Retinex and dark channel prior using Taylor series expansion

Qunfang

Jia

et al. 2021

Computer Vision and Image Understanding

View full text Add to dashboard Cite

“…[9], and Zhu et al. [10] explore alternative methodologies, including the incorporation of recurring patches, haze lines, colour lines, and colour plane, for air‐light estimation. The utilization of larger pixel sets in air‐light estimation exhibits enhanced performance.…”

Section: Introductionmentioning

confidence: 99%

“…However, relying on the brightest pixels for AE might produce colour distortion in dehazed images, since these pixels could represent luminous objects rather than the true air-light. In order to overcome this limitation, Bahat et al [7], Berman et al [8], Sulami et al [9], and Zhu et al [10] explore alternative methodologies, including the incorporation of recurring patches, haze lines, colour lines, and colour plane, for air-light estimation. The utilization of larger pixel sets in air-light estimation exhibits enhanced performance.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Global strategy for robust air‐light estimation in dehazing

Zhu,

Zhang

2024

Electronics Letters

View full text Add to dashboard Cite

This letter proposes a novel approach to enhance the robustness of air‐light estimation for dehazing. Unlike most existing methods, it employs a global strategy, considering all pixels instead of specific individual ones, for recovering air‐light. Through an iterative algorithm via the Gray World (GW), the authors extract the air‐light orientation from the entire image. Next, a global detail‐preserving algorithm is designed to determine the optimal magnitude of air‐light. Experimental results on a diverse set of hazy images reveal that the authors’ method outperforms other state‐of‐the‐art alternatives, highlighting the advantage of air‐light estimation using the entire image information.

show abstract

“…This method includes DCP followed by CLAHE and DWT for overall contrast enhancement and virtual histogram stretching of an image. The DCP method estimates atmospheric light's thickness and extracts it for a haze‐free image 5,18,19 . The DCP method gives excellent results regarding the image's contrast, but it destroys its actual color.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligent based smart system for safe mining during foggy weather

Kumari

Choudhary

Mishra

et al. 2021

Concurrency and Computation

View full text Add to dashboard Cite

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.

show abstract

Atmospheric light estimation in hazy images based on color-plane model

Cited by 11 publications

References 16 publications

Nighttime image dehazing based on Retinex and dark channel prior using Taylor series expansion

Nighttime image dehazing based on Retinex and dark channel prior using Taylor series expansion

Global strategy for robust air‐light estimation in dehazing

Artificial intelligent based smart system for safe mining during foggy weather

Contact Info

Product

Resources

About