Estimating image illuminant color based on gray world assumption

Nguyen, M. T.; A., C. L. D.; Kwok, Ngaiming

doi:10.1109/cisp.2011.6100321

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…GWA, which assumes the average albedos of three colour channels of a scene tend to be a same constant and are achromatic (gray) [48], has been successfully used for colour cast correction [49]- [51]. In this work, we make use of GWA to restrict the uncertainty of EASM by considering each local patch in an input image as a small scene.…”

Section: A Priori Constraintmentioning

confidence: 99%

IDE: Image Dehazing and Exposure Using an Enhanced Atmospheric Scattering Model

Ding

Ren

et al. 2021

IEEE Trans. on Image Process.

145

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Atmospheric scattering model (ASM) is one of the most widely used model to describe the imaging processing of hazy images. However, we found that ASM has an intrinsic limitation which leads to a dim effect in the recovered results. In this paper, by introducing a new parameter, i.e., light absorption coefficient, into ASM, an enhanced ASM (EASM) is attained, which can address the dim effect and better model outdoor hazy scenes. Relying on this EASM, a simple yet effective grayworld-assumption-based technique called IDE is then developed to enhance the visibility of hazy images. Experimental results show that IDE eliminates the dim effect and exhibits excellent dehazing performance. It is worth mentioning that IDE does not require any training process or extra information related to scene depth, which makes it very fast and robust. Moreover, the global stretch strategy used in IDE can effectively avoid some undesirable effects in recovery results, e.g., over-enhancement, over-saturation, and mist residue, etc. Comparison between the proposed IDE and other state-of-the-art techniques reveals the superiority of IDE in terms of both dehazing quality and efficiency over all the comparable techniques.

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Section: A Priori Constraintmentioning

confidence: 99%

IDE: Image Dehazing and Exposure Using an Enhanced Atmospheric Scattering Model

Ding

Ren

et al. 2021

IEEE Trans. on Image Process.

145

View full text Add to dashboard Cite

show abstract

“…The essence of image dehazing is to exclude the brightness interference caused by atmospheric light during the imaging procedure. The first constraint is based on the fact that the average brightness of a high-quality image tends to have a specific value µ [58]. That is to say, we need to find a combination of k and A to minimise…”

Section: B Joint Optimizationmentioning

confidence: 99%

IDRLP: Image Dehazing Using Region Line Prior

Ding

Guo

et al. 2021

IEEE Trans. on Image Process.

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In this work, a novel and ultra-robust single image dehazing method called IDRLP is proposed. It is observed that when an image is divided into n regions, with each region having a similar scene depth, the brightness of both the hazy image and its haze-free correspondence are positively related with the scene depth. Based on this observation, this work determines that the hazy input and its haze-free correspondence exhibit a quasi-linear relationship after performing this region segmentation, which is named as region line prior (RLP). By combining RLP and the atmospheric scattering model (ASM), a recovery formula (RF) can be easily obtained with only two unknown parameters, i.e., the slope of the linear function and the atmospheric light. A 2-D joint optimization function considering two constraints is then designed to seek the solution of RF. Unlike other comparable works, this "joint optimization" strategy makes efficient use of the information across the entire image, leading to more accurate results with ultra-high robustness. Finally, a guided filter is introduced in RF to eliminate the adverse interference caused by the region segmentation. The proposed RLP and IDRLP are evaluated from various perspectives and compared with related state-of-the-art techniques. Extensive analysis verifies the superiority of IDRLP over state-of-the-art image dehazing techniques in terms of both the recovery quality and efficiency. A software release is available at https://sites.google.com/site/renwenqi888/.

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Computational Deep Intelligence Vision Sensing for Nutrient Content Estimation in Agricultural Automation

Sulistyo

Woo

et al. 2018

IEEE Trans. Automat. Sci. Eng.

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Estimating image illuminant color based on gray world assumption

Cited by 4 publications

References 16 publications

IDE: Image Dehazing and Exposure Using an Enhanced Atmospheric Scattering Model

IDE: Image Dehazing and Exposure Using an Enhanced Atmospheric Scattering Model

IDRLP: Image Dehazing Using Region Line Prior

Computational Deep Intelligence Vision Sensing for Nutrient Content Estimation in Agricultural Automation

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