Sandstorm image enhancement based on YUV space

Gao, Guxue; Lai, Huicheng; Liu, YueQin; Wang, Lie-Jun; Jia, Zhenhong

doi:10.1016/j.ijleo.2020.165659

Cited by 21 publications

(22 citation statements)

References 14 publications

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“…The experimental equipment was performed on a server configured with 10th generation Intel Core i7, 3.60 GHz, Nvidia GeForce RTX 1660s. [20] processing, but the colour is reddish and the overall brightness is too large. As shown in Figures 6(f)-9(f), through our proposed method, the colour cast of the sand-dust images have been corrected, and the image contrast and lightness are both significantly enhanced, and richly detailed images with vivid colour information are obtained.…”

Section: Data Set and Experiments Settingmentioning

confidence: 99%

“…To solve the above problems such as colour cast, contrast reduction, and loss of details, many researchers [4][5][6][7][8][9][10][11][12][13][14] have proposed corresponding image enhancement schemes in adverse scenes such as haze, uneven lighting, and underwater, but these algorithms cannot effectively and directly enhancing sand-dust images. Although in recent years, some algorithms [15][16][17][18][19][20][21][22][23][24][25][26][27] that specifically enhance sand-dust images have been proposed, the results still suffer from halos, colour cast, blue artefacts, and saturation degradation. Computer vision applications such as feature point extraction and object detection in sand-dust environments require not only vivid colours but also rich image details and high contrast.…”

Section: Introductionmentioning

confidence: 99%

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Colour balance and contrast stretching for sand‐dust image enhancement

Hua

Min

et al. 2022

IET Image Processing

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The increasingly frequent sand-dust weather in the inland areas seriously affects outdoor vision applications, especially autonomous vehicles and security monitoring. To moderate the image's colour cast and poor contrast caused by sand-dust weather, an effective approach is proposed in this study to enhance the sand-dust images. First, the original degraded image's colour cast is corrected by a new colour balance and compensation formula, which compensates the blue and green channel information through numerous yellow channel information caused by sand-dust scattering before white balance. Next, in order to avoid the new colour deviation, the corrected image is converted from the RGB colour space to the HSV colour space and use the CLAHE to enhance the V component to improve the contrast. Then, a nonlinear gain function is defined to further adaptively sharpen the V component to enhance image details. Finally, the S component is stretched to improve image saturation. The extensive qualitative and quantitative evaluation shows that this method can improve the image edge clarity and contrast, restore good colour fidelity for all sand-dust images tested. The verification also proves that this method is of much significance in improving the feature point extraction and the target detection results in the sand-dust weather.

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Section: Data Set and Experiments Settingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Colour balance and contrast stretching for sand‐dust image enhancement

Hua

Min

et al. 2022

IET Image Processing

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show abstract

“…Recently, numerous sand dust image methods have been proposed [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14] which can be roughly divided into two classes. One line of research is the restoration algorithms, and another line is tries to modify image pixel values to enhance visual quality.…”

Section: A Single Image Sand Dust Removal Algorithmsmentioning

confidence: 99%

“…To address this issue, many studies have been proposed [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], which can be roughly divided into two strategies including image restoration [2], [3], [4], [5], [6] and image enhancement [7], [8], [9], [10], [11], [12], [13], [14]. The sand dust image restoration algorithms are designed based on the atmospheric scattering model.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive benchmark analysis for sand dust image reconstruction

Si¹,

Yang²,

Guo³

et al. 2022

Preprint

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Numerous sand dust image enhancement algorithms have been proposed in recent years. To our best acknowledge, however, most methods evaluated their performance with noreference way using few selected real-world images from internet. It is unclear how to quantitatively analysis the performance of the algorithms in a supervised way and how we could gauge the progress in the field. Moreover, due to the absence of largescale benchmark datasets, there are no well-known reports of data-driven based method for sand dust image enhancement up till now. To advance the development of deep learning-based algorithms for sand dust image reconstruction, while enabling supervised objective evaluation of algorithm performance. In this paper, we presented a comprehensive perceptual study and analysis of real-world sand dust images, then constructed a Sand-dust Image Reconstruction Benchmark (SIRB) for training Convolutional Neural Networks (CNNs) and evaluating algorithms performance. In addition, we adopted the existing image transformation neural network trained on SIRB as baseline to illustrate the generalization of SIRB for training CNNs. Finally, we conducted the qualitative and quantitative evaluation to demonstrate the performance and limitations of the state-of-thearts (SOTA), which shed light on future research in sand dust image reconstruction.

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“…Image enhancement algorithms improve the clarity of sand dust degraded images via the prior image processing theory. Such as balancing the histogram equalization 19 , 21 , guided filtering 22 , 26 , and Retinex-based filtering 18 , 27 .…”

Section: Introductionmentioning

confidence: 99%

Sand dust image visibility enhancement algorithm via fusion strategy

Yang

Liu

2022

Sci Rep

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The outdoor images captured in sand dust weather often suffer from poor contrast and color distortion, which seriously interfere with the performance of intelligent information processing systems. To solve the issues, a novel enhancement algorithm based on fusion strategy is proposed in this paper. It includes two components in sequence: sand removal via the improved Gaussian model-based color correction algorithm and dust elimination using the residual-based convolutional neural network (CNN). Theoretical analysis and experimental results show that compared with the prior sand dust image enhancement methods, the proposed fusion strategy can effectively correct the overall yellowing hue and remove the dust haze disturbance, which provides a constructive idea for the future development of sand dust image enhancement.

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Sandstorm image enhancement based on YUV space

Cited by 21 publications

References 14 publications

Colour balance and contrast stretching for sand‐dust image enhancement

Colour balance and contrast stretching for sand‐dust image enhancement

A comprehensive benchmark analysis for sand dust image reconstruction

Sand dust image visibility enhancement algorithm via fusion strategy

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