Improvement of underwater colour correction using standard deviation ratio

Moon, Sun-A; Lee, Hyunseop; Eom, Il Kyu

doi:10.1049/el.2020.1796

Cited by 8 publications

(5 citation statements)

References 12 publications

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“…The Underwater White Balance (UWB) method [2] was early attempt to recompense the loss in the blue and red channels based on the green channel. Later, the Improved UWB (IUWB) methods [7] improved the color compensation based on the standard deviation ratio and proposed a mean adjustment method to ensure the means of the blue and red channels were equivalent to that of the green channel. Besides, the Color Filter Array (CFA) based method [6] proposed to compensate the red channel based on the blue and green channels.…”

Section: Literature Reviewmentioning

confidence: 99%

Recompense the Color Loss for Underwater Image using Generalized Color Compensation (GCC) Technique

Yao

Bade

Waheed

2022

J. Phys.: Conf. Ser.

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The light absorption of the water medium often caused the underwater image enhancement to become challenging due to the loss in color channels where the red color vanished faster than others. The color loss often affected the underwater image quality especially having the color cast like greenish, bluish, and yellowish. Therefore, this paper introduced a Generalized Color Compensation (GCC) technique which will recompense the color loss based on maximum channel and then remove the color cast using the mean adjustment. For the experiments conducted, the GCC technique improved the image contrast successfully. The experiments showed that the proposed technique was able to remove the color cast effectively regardless of greenish, bluish, and yellowish and improve the underwater image quality.

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Section: Literature Reviewmentioning

confidence: 99%

Recompense the Color Loss for Underwater Image using Generalized Color Compensation (GCC) Technique

Yao

Bade

Waheed

2022

J. Phys.: Conf. Ser.

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“…Thus, the red color values decrease. δ c md was successfully exploited in various white balance methods [29][30][31] for underwater image color correction. This factor plays an important role in various color-correction algorithms.…”

Section: A Initial Color Correctionmentioning

confidence: 99%

“…In our previous works, we presented an improved underwater color correction method using the standard deviation ratio of color channels [30,31]. Although color channels have the same mean, their standard deviations can be different.…”

Section: A Initial Color Correctionmentioning

confidence: 99%

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Sand-Dust Image Enhancement Using Successive Color Balance With Coincident Chromatic Histogram

Park

Eom

2021

IEEE Access

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Outdoor images in sand-dust environments play an adverse role in various remote-based computer vision tasks because captured sand-dust images have severe color casts, low contrast, and poor visibility. However, although sand-dust image restoration is as important as haze removal and underwater image enhancement, it has not been sufficiently studied. In this paper, we present a novel color balance algorithm for sand-dust image enhancement. The aim of the proposed enhancement method is to obtain a coincident chromatic histogram. First, we introduce a pixel-adaptive color correction method using the mean and standard deviation of chromatic histograms. Pixels of each color component are adjusted based on the statistical characteristics of the green component. Second, a green-mean-preserving color normalization technique is presented. However, using the mean of red and blue components as the mean of the green can result in an undesirable output because the red or blue components of many sand-dust images have a narrow histogram with a high peak. To address this problem, we propose a histogram shifting algorithm that makes the red and blue histograms overlap the green histogram as much as possible. Based on this algorithm, bluish or reddish artifacts of the enhanced image can be reduced. Finally, image adjustment is exploited to improve the brightness of the sand-dust image. We performed intensive experiments for various sand-dust images and compared the performance of the proposed method with those of state-of-the-art enhancement methods. The simulation results indicate that the proposed enhancement scheme outperforms the existing approaches in terms of both subjective and objective qualities. INDEX TERMS Sand-dust image enhancement, color normalization, green-mean preserving, maximum overlapped histogram, coincident chromatic histogram.

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“…Some researchers in this field attempted the fusion‐based underwater image enhancement methods using color balance (CB) and multiscale techniques 23‐25 . Subsequently, contrast‐limited adaptive histogram equalization (CLAHE) and percentile technique, 26 multiscale retinex (MSR) algorithm, 27 screened Poisson equation (SPE), 28,29 histogram stretching, 30 Rayleigh distribution, 31 adaptive attenuation‐curve prior, 32 color correction and bi‐interval contrast enhancement, 33 standard deviation ratio, 34 and retinex‐based 35 method have been investigated for enhancing the underwater images without an imaging model. Model‐independent techniques have the lacuna of over‐saturation and partial dehazing of underwater image.…”

Section: Literature Reviewmentioning

confidence: 99%

Multiscale decomposition and fusion‐based color contrast restoration for various water‐colored environments

Sivamani

Nedumaran

2021

Color Research & Application

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Underwater image processing is a vast area of research to identify blurred objects in the ocean or sea environments. The light absorbed and scattered by the underwater medium create artifacts in the acquired images due to low‐light effects, poor visibility, darkness, illumination variation, and color contrast degradation. In this research work, a novel optical image preprocessing framework was proposed to restore the color contrast of images obtained from various water‐colored environments. Domain transform‐based multiscale image decomposition method was used to extract the base, residual, and detail layers. Then, the smoothed image was reconstructed from the base layer by applying the adaptive iterative backward‐projection algorithm. To obtain dehazed images, a multiscale fusion‐based algorithm was developed, which fused the detail layers and reconstructed the images. The outcome of the proposed framework revealed that the contrast degradation and low‐light‐related artifacts present in the acquired images from various water‐colored environments were found to be considerably reduced. The experimental results of the proposed method exhibit scalable improvements over the other methods in terms of visual perception and estimated performance metrics (underwater image quality measure = 6.1186; peak signal‐to‐noise ratio = 26.68; feature points matching = 96 points; semantic image segmentation accuracy = 0.8465 and processing time of 0.11 seconds on a single image). The novelty of this method relies on the fusion of well‐established techniques, which outperformed the advanced techniques like deep learning method in terms of simplicity, speed, performance, datasets, image resolution, and applications.

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Improvement of underwater colour correction using standard deviation ratio

Cited by 8 publications

References 12 publications

Recompense the Color Loss for Underwater Image using Generalized Color Compensation (GCC) Technique

Recompense the Color Loss for Underwater Image using Generalized Color Compensation (GCC) Technique

Sand-Dust Image Enhancement Using Successive Color Balance With Coincident Chromatic Histogram

Multiscale decomposition and fusion‐based color contrast restoration for various water‐colored environments

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