A comprehensive benchmark analysis for sand dust image reconstruction

Si, Yazhong; Yang, Fan; Zhang, Wei; Yang, Yipu

doi:10.1016/j.jvcir.2022.103638

Cited by 14 publications

(4 citation statements)

References 40 publications

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“…However, deep learning methods have not been well studied in image dedusting, primarily due to the limitation of the dataset. Recently, the sand-dust images were synthesized by Si et al [23], who conducted quantitative and qualitative evaluations of the synthesized images. However, compared with the real sand-dust image, the synthetic sand-dust image still has a big gap.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

“…Te lack of ground truth images makes the reference metrics PSNR and SSIM unusable. Te literature [23] explored the nonreference metrics spatial-spectral entropy-based quality SSEQ [41] and blind image quality index BIQI [42] to evaluate the sand-dust images and proved their validity. In this study, we used these two metrics to (d) Shades of Gray [35], (e) Gray-World [33], (f ) OTM [16], and (g) Our.…”

Section: Quantitative Evaluation Of the Sand-dust Image Enhancement E...mentioning

confidence: 99%

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Two-Step Unsupervised Approach for Sand-Dust Image Enhancement

Gao

Lai

Jia

2023

International Journal of Intelligent Systems

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In sand-dust environments, light is scattered and absorbed, and sand-dust images thus suffer from severe image degradation problems, such as color shifts, low contrast, and blurred details. To address these problems, we propose a two-step unsupervised sand-dust image enhancement algorithm. In the first step, a convenient and competent color correction method is put forward to solve the color shift problem. Considering the wavelength attenuation features of sand-dust images, a linear stretching and blue channel compensation method is designed, and an adaptive color shift correction factor is developed to remove the color shift. In the second step, to enhance the clarity and details of the images, an unsupervised generative adversarial network is proposed, which does not require pairs of data for training. To reduce detail loss, the detail enhancement branch is designed, and the generator considers to more details through the constructed coarse-grained and fine-grained discriminators. The introduced multiscale perceptual loss promotes the image fidelity well. Experiments show that the proposed method achieves better color correction, enhances image details and clarity, has a better subjective effect, and outperforms existing sand-dust image enhancement methods both quantitatively and qualitatively. Similarly, our method promotes the application capability of the target detection algorithm and also has a good enhancement effect on underwater images and haze images.

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Section: Deep Learning Methodsmentioning

confidence: 99%

Section: Quantitative Evaluation Of the Sand-dust Image Enhancement E...mentioning

confidence: 99%

Two-Step Unsupervised Approach for Sand-Dust Image Enhancement

Gao

Lai

Jia

2023

International Journal of Intelligent Systems

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“…The first strategy is to estimate the transmittance and atmospheric light in the atmospheric scattering model through deep learning [16], [17]. The second strategy is to establish an end-to-end network from sand-dust images to sand-dust-free images through deep learning [15], [18], [19]. Notably, the construction of a network to improve the definition of dust images based on deep learning is a data-driven method that requires comprehensive datasets.…”

Section: Related Workmentioning

confidence: 99%

Online Low-Light Sand-Dust Video Enhancement Using Adaptive Dynamic Brightness Correction and a Rolling Guidance Filter

Jia

Yang

et al. 2024

IEEE Trans. Multimedia

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Sand-dust videos obtained in a low-light environment are characterized by low contrast, nonuniform illumination, color cast, and considerable noise. To realize sand-dust removal and brightness enhancement simultaneously, this paper proposes an online low-light sand-dust video enhancement method using adaptive dynamic brightness correction and a rolling guidance filter. The proposed dual-threshold interframe detection strategy involves two methods to treat low-light sand-dust video frames. The first method involves two components: an adaptive dynamic brightness correction algorithm to correct the color deviation of the low-light video frame and improve its brightness and a rolling guidance filter combined with guided image filtering to enhance the frame details. The second method enhances the quality of the incoming frame by reducing the amount of calculation. The first frame of the video is processed using the first method. The processing method of each subsequent frame is determined according to its interframe detection value with the buffer frame. Through qualitative and quantitative comprehensive experiments on low-light sand-dust images and videos, the performance of the proposed method is compared with those of state-of-the-art methods. The proposed method for frame quality improvement achieves the best visual effect in enhancing the quality of low-light sand-dust images, as indicated by the best objective evaluation indicators. Moreover, compared with the framewise enhancement method, the video processing efficiency associated with the dualthreshold interframe detection strategy is 2.77 times higher.

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“…When taking images or videos in dusty sand weather, there are usually various impairments in the quality of vision and detection of objects. Generally, the image appears dimmed in dusty sand and has low color contrast, poor visibility, and new high color tones of yellowish or reddish [1][2][3][4][5][6][7]. Technically, the low image quality in terrible atmospheric sandstorms is because the sand dust particles scatter and absorb a specific light spectrum.…”

Section: Introductionmentioning

confidence: 99%

Fast Dust Sand Image Enhancement Based on Color Correction and New Fuzzy Intensification Operators

Hakem Alsaeedi,

Alazzawi,

Mohammed Hadi

2023

Int J Innov Comp

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Images captured in dusty environments suffering from poor visibility and quality. Enhancement of these images such as sand dust images plays a critical role in various atmospheric optics applications. In this work, proposed a new model based on Color Correction and New Fuzzy Intensification Operators to enhance san dust images. The proposed model consists of three phases: correction of color shift, removal of haze, and enhancement of contrast and brightness. The color shift is corrected using a fuzzy intensification operator to adjust the values of U and V in the YUV color space. The Adaptive Dark Channel Prior (A-DCP) is used for haze removal. The stretching contrast and improving image brightness are based on Contrast Limited Adaptive Histogram Equalization (CLAHE). The proposed model tests and evaluates through many real sand dust images. The experimental results show that the proposed solution is outperformed the current studies in terms of effectively removing the red and yellow cast and provides high quality and quantity dust images.

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A comprehensive benchmark analysis for sand dust image reconstruction

Cited by 14 publications

References 40 publications

Two-Step Unsupervised Approach for Sand-Dust Image Enhancement

Two-Step Unsupervised Approach for Sand-Dust Image Enhancement

Online Low-Light Sand-Dust Video Enhancement Using Adaptive Dynamic Brightness Correction and a Rolling Guidance Filter

Fast Dust Sand Image Enhancement Based on Color Correction and New Fuzzy Intensification Operators

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