Sand dust image visibility enhancement algorithm via fusion strategy

Si, Yazhong; Yang, Fan; Liu, Zhao

doi:10.1038/s41598-022-17530-3

Cited by 9 publications

(6 citation statements)

References 34 publications

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“…Applying the minimum operator on the three color channels + 𝐴 (6) where t0 is typically 0.1 to prevent division by very small terms when the transmission is near zero. Furthermore, ( 5) is modified to get,…”

Section: A Transmisstion and Airlight Estimationmentioning

confidence: 99%

“…This suggested algorithm works by adjusting the gain of RGB channels so that the corresponding chrominance channels in YUV colorspace achieve zero mean (achromaticity). Si et al [6] have proposed to shape the RGB histograms by forcing the mean and variance of each channel to be the same after normalization. This approach only seems to work for images with low degree of color imbalance.…”

Section: B Color Cast Correctionmentioning

confidence: 99%

“…It can be seen that the proposed approach yields a crisp transmission map. The illumination channel, 'I' is then finally restored using (6).…”

Section: B Transmission Estimation and Radiance Restorationmentioning

confidence: 99%

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Efficient Framework for Real-Time Color Cast Correction and Dehazing Using Online Algorithms to Approximate Image Statistics

Bilal,

Masud,

Hanif

2024

IEEE Access

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Image dehazing is a crucial task in the early stages of real-time image processing pipelines used in applications such as surveillance and advance driver assistance systems. Dehazing algorithms mitigate the spatially selective degradation of image details caused by natural phenomenon such as fog and sandstorms. The problem is exacerbated in the presence of color cast which can affect the color-sensitive processing in the downstream tasks. In order to correct these two aberrations, we have proposed a lightweight algorithm which is not only quantitively more effective than the state-of-the-art works but also uses minimal computational resources. Specifically, it has been proposed to tackle both aforementioned problems by processing luminance and chrominance channels separately through a custom resource-efficient colorspace transform. Moreover, it has been proposed to employ online calculation of the relevant video stream statistics to estimate the degradation model over several temporally adjacent frames. This approach not only reduces the hardware resource utilization when implemented inside the video processing pipeline but also reduces the flicker effect observed when frames are processed individually. It has been demonstrated through quantitative analysis on standard datasets that the proposed approach either works at par or better than the reference works in terms of image quality metrics. Furthermore, the proposed framework has been developed as a real-time video processing system on an FPGA platform. The synthesis results of this implementation suggest that the proposed framework achieves this performance using minimal logic resources.

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Section: A Transmisstion and Airlight Estimationmentioning

confidence: 99%

Section: B Color Cast Correctionmentioning

confidence: 99%

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Efficient Framework for Real-Time Color Cast Correction and Dehazing Using Online Algorithms to Approximate Image Statistics

Bilal,

Masud,

Hanif

2024

IEEE Access

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“…The sand-dust dataset was acquired from images by Yazhong Si et al [16] and obtained via a Google image search. Haze images were obtained from O-haze [17] and GridDe-hazeNet.…”

Section: Dataset and Computer Specificationmentioning

confidence: 99%

“…The sand-dust data presented in this study are openly available in references [3,4,16], and the haze dataset is from RESIDE to be freely utilized at https://sites.google. com/view/reside-dehaze-datasets/reside-v0 (accessed on 18 April 2024).…”

Section: Data Availability Statementmentioning

confidence: 99%

Enhancing Surveillance Vision with Multi-Layer Deep Learning Representation

Son,

Lee

2024

Mathematics

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This paper aimed to develop a method for generating sand–dust removal and dehazed images utilizing CycleGAN, facilitating object identification on roads under adverse weather conditions such as heavy dust or haze, which severely impair visibility. Initially, the study addressed the scarcity of paired image sets for training by employing unpaired CycleGAN training. The CycleGAN training module incorporates hierarchical single-scale Retinex (SSR) images with varying sigma sizes, facilitating multiple-scaled trainings. Refining the training data into detailed hierarchical layers for virtual paired training enhances the performance of CycleGAN training. Conventional sand–dust removal or dehazing algorithms, alongside deep learning methods, encounter challenges in simultaneously addressing sand–dust removal and dehazing with a singular algorithm. Such algorithms often necessitate resetting hyperparameters to process images from both scenarios. To overcome this limitation, we proposed a unified approach for removing sand–dust and haze phenomena using a single model, leveraging images processed hierarchically with SSR. The image quality and image sharpness metrics of the proposed method were BRIQUE, PIQE, CEIQ, MCMA, LPC-SI, and S3. In sand–dust environments, the proposed method achieved the highest scores, with an average of 21.52 in BRISQUE, 0.724 in MCMA, and 0.968 in LPC-SI compared to conventional methods. For haze images, the proposed method outperformed conventional methods with an average of 3.458 in CEIQ, 0.967 in LPC-SI, and 0.243 in S3. The images generated via this proposed method demonstrated superior performance in image quality and sharpness evaluation compared to conventional algorithms. The outcomes of this study hold particular relevance for camera images utilized in automobiles, especially in the context of self-driving cars or CCTV surveillance systems.

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