Color balance and sand-dust image enhancement in lab space

Gao, Guxue; Lai, Huicheng; Wang, LieJue; Jia, Zhenhong

doi:10.1007/s11042-022-12276-6

Cited by 13 publications

(4 citation statements)

References 23 publications

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“…Li et al [4] mapped the sand-dust image to the Lab color space, achieved color correction by compensating and stretching the a and b channels, and sharpened and filtered the L channel to enhance the detailed. Inspired by underwater image enhancement, some scholars [5][6][7][8] compensated the blue channel based on the green channel information to correct the color deviation of the sand-dust image. In order to further enhance the contrast of the sand-dust image, Shi et al [9] proposed a limited contrast adaptive histogram equalization algorithm based on the normalized gamma transform for the L channel.…”

Section: Related Workmentioning

confidence: 99%

An improved pix2pix generative adversarial networks for sand-dust image enhancement

Hua,

Qi,

Yang

et al. 2024

SIViP

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The frequent sand-dust weather in inland areas has severely affected the local outdoor computer vision applications. Different from the previous ideas of the sand-dust image enhancement algorithm, this paper proposes a generative adversarial network to enhance the sand-dust images. We improve the classic pix2pix network by introducing the dual attention mechanism to the U-net architecture and improve the loss function of the generator through Smooth L1 and SSIM to further enhance the color reproduction, detail features, the structural similarity, and the convergence speed of the generator. In addition, we also publish the first artificially synthesized sand-dust image data set online. The experimental results show that the enhancement method proposed in this paper has obvious advantages in both artificially synthesized images and natural real images, compared with the current traditional sand-dust enhancement algorithms and the previous network models.

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Section: Related Workmentioning

confidence: 99%

An improved pix2pix generative adversarial networks for sand-dust image enhancement

Hua,

Qi,

Yang

et al. 2024

SIViP

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“…In particular, in challenging weather like sandy weather, the acquired images often display significant color disparities [14]. This not only hampers image enhancement but also impacts its suitability for computer vision tasks such as image stitching [15]. To solve these problems, it is necessary to perform color balancing on the acquired image to eliminate or reduce the change in an object's color due to the acquisition process, so as to enhance the original characteristics of the image.…”

Section: Color Balance Correctionmentioning

confidence: 99%

Non-Uniform-Illumination Image Enhancement Algorithm Based on Retinex Theory

Ji,

Guo,

Zhang

et al. 2023

Applied Sciences

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To address the issues of fuzzy scene details, reduced definition, and poor visibility in images captured under non-uniform lighting conditions, this paper presents an algorithm for effectively enhancing such images. Firstly, an adaptive color balance method is employed to address the color differences in low-light images, ensuring a more uniform color distribution and yielding a low-light image with improved color consistency. Subsequently, the image obtained is transformed from the RGB space to the HSV space, wherein the multi-scale Gaussian function is utilized in conjunction with the Retinex theory to accurately extract the lighting components and reflection components. To further enhance the image quality, the lighting components are categorized into high-light areas and low-light areas based on their pixel mean values. The low-light areas undergo improvement through an enhanced adaptive gamma correction algorithm, while the high-light areas are enhanced using the Weber–Fechner law for optimal results. Then, each block area of the image is weighted and fused, leading to its conversion back to the RGB space. And a multi-scale detail enhancement algorithm is utilized to further enhance image details. Through comprehensive experiments comparing various methods based on subjective visual perception and objective quality metrics, the algorithm proposed in this paper convincingly demonstrates its ability to effectively enhance the brightness of non-uniformly illuminated areas. Moreover, the algorithm successfully retains details in high-light regions while minimizing the impact of non-uniform illumination on the overall image quality.

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“…It provided a reasonable restoring performance in terms of both subjective and objective measures. Gao et al [ 28 ] proposed a color balance technique to restore sand-dust images in the Lab color space. In the first step, the lost value of the blue channel was compensated using the green channel.…”

Section: Related Workmentioning

confidence: 99%

Sand-Dust Image Enhancement Using Chromatic Variance Consistency and Gamma Correction-Based Dehazing

Jeon

Park

Eom

2022

Sensors

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In sand-dust environments, the low quality of images captured outdoors adversely affects many remote-based image processing and computer vision systems, because of severe color casts, low contrast, and poor visibility of sand-dust images. In such cases, conventional color correction methods do not guarantee appropriate performance in outdoor computer vision applications. In this paper, we present a novel color correction and dehazing algorithm for sand-dust image enhancement. First, we propose an effective color correction method that preserves the consistency of the chromatic variances and maintains the coincidence of the chromatic means. Next, a transmission map for image dehazing is estimated using the gamma correction for the enhancement of color-corrected sand-dust images. Finally, a cross-correlation-based chromatic histogram shift algorithm is proposed to reduce the reddish artifacts in the enhanced images. We performed extensive experiments for various sand-dust images and compared the performance of the proposed method to that of several existing state-of-the-art enhancement methods. The simulation results indicated that the proposed enhancement scheme outperforms the existing approaches in terms of both subjective and objective qualities.

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Color balance and sand-dust image enhancement in lab space

Cited by 13 publications

References 23 publications

An improved pix2pix generative adversarial networks for sand-dust image enhancement

An improved pix2pix generative adversarial networks for sand-dust image enhancement

Non-Uniform-Illumination Image Enhancement Algorithm Based on Retinex Theory

Sand-Dust Image Enhancement Using Chromatic Variance Consistency and Gamma Correction-Based Dehazing

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