Efficient Color Correction Using Normalized Singular Value for Duststorm Image Enhancement

A duststorm image has a reddish or yellowish color cast. Though a duststorm image and a hazy image are obtained using the same process, a hazy image has no color distortion as it has not been disturbed by particles, but a duststorm image has color distortion owing to an imbalance in the color channel, which is disturbed by sand particles. As a result, a duststorm image has a degraded color channel, which is rare in certain channels. Therefore, a color balance step is needed to enhance a duststorm image naturally. This study goes through two steps to improve a duststorm image. The first is a color balance step using singular value decomposition (SVD). The singular value shows the image’s diversity features such as contrast. A duststorm image has a distorted color channel and it has a different singular value on each color channel. In a low-contrast image, the singular value is low and vice versa. Therefore, if using the channel’s singular value, the color channels can be balanced. Because the color balanced image has a similar feature to the haze image, a dehazing step is needed to improve the balanced image. In general, the dark channel prior (DCP) is frequently applied in the dehazing step. However, the existing DCP method has a halo effect similar to an over-enhanced image due to a dark channel and a patch image. According to this point, this study proposes to adjustable DCP (ADCP). In the experiment results, the proposed method was superior to state-of-the-art methods both subjectively and objectively.

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Low-contrast satellite images enhancement using discrete cosine transform pyramid and singular value decomposition

IET Image Processing ◽

10.1049/iet-ipr.2013.0083 ◽

2013 ◽

Vol 7 (5) ◽

pp. 472-483 ◽

Cited By ~ 18

Author(s):

Randa Atta ◽

Mohammad Ghanbari

Keyword(s):

Singular Value Decomposition ◽

Discrete Cosine Transform ◽

Satellite Images ◽

Singular Value ◽

Cosine Transform ◽

Low Contrast ◽

Value Decomposition

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An Image Defogging Approach Based on a Constrained Energy Functional under Bayesian and Variation Theories

Mathematical Problems in Engineering ◽

10.1155/2015/647080 ◽

2015 ◽

Vol 2015 ◽

pp. 1-14

Author(s):

Li Zhou ◽

Du-Yan Bi ◽

Lin-Yuan He

Keyword(s):

State Of The Art ◽

Regularization Parameter ◽

Energy Functional ◽

Maximum A Posteriori ◽

A Posteriori ◽

Unknown Quantity ◽

Low Contrast ◽

Multiplicative Noises ◽

Image Defogging ◽

Dark Channel

Hazy images produce negative influences on visual applications in the open air since they are in poor visibility with low contrast and whitening color. Numerous existing methods tend to derive a totally rough estimate of scene depth. Unlike previous work, we focus on the probability distribution of depth that is considered as a scene prior. Inspired by the denoising work of multiplicative noises, the inverse problem for hazy removal is recast as deriving the optimal difference between scene irradiance and the airlight from a constrained energy functional under Bayesian and variation theories. Logarithmic maximum a posteriori estimator and a mixed regularization term are introduced to formulate the energy functional framework where the regularization parameter is adaptively selected. The airlight, another unknown quantity, is inferred precisely under a geometric constraint and dark channel prior. With these two estimates, scene irradiance can be recovered. The experimental results on a series of hazy images reveal that, in comparison with several relevant and most state-of-the-art approaches, the proposed method outperforms in terms of vivid color and appropriate contrast.

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Efficient Sandstorm Image Enhancement Using the Normalized Eigenvalue and Adaptive Dark Channel Prior

Technologies ◽

10.3390/technologies9040101 ◽

2021 ◽

Vol 9 (4) ◽

pp. 101

Author(s):

Ho Sang Lee

Keyword(s):

Image Enhancement ◽

Dark Channel Prior ◽

Color Channel ◽

Dark Channel ◽

Degraded Image ◽

Weak Points ◽

Sand Particles ◽

The Difference ◽

Color Cast ◽

Sand Dust

A sandstorm image has features similar to those of a hazy image with regard to the obtaining process. However, the difference between a sand dust image and a hazy image is the color channel balance. In general, a hazy image has no color cast and has a balanced color channel with fog and dust. However, a sand dust image has a yellowish or reddish color cast due to sand particles, which cause the color channels to degrade. When the sand dust image is enhanced without color channel compensation, the improved image also has a new color cast. Therefore, to enhance the sandstorm image naturally without a color cast, the color channel compensation step is needed. Thus, to balance the degraded color channel, this paper proposes the color balance method using each color channel’s eigenvalue. The eigenvalue reflects the image’s features. The degraded image and the undegraded image have different eigenvalues on each color channel. Therefore, if using the eigenvalue of each color channel, the degraded image can be improved naturally and balanced. Due to the color-balanced image having the same features as the hazy image, this work, to improve the hazy image, uses dehazing methods such as the dark channel prior (DCP) method. However, because the ordinary DCP method has weak points, this work proposes a compensated dark channel prior and names it the adaptive DCP (ADCP) method. The proposed method is objectively and subjectively superior to existing methods when applied to various images.

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Radiographic X-ray Images Enhancement with Edge Preservation using Singular Value Decomposition

International Journal of Computational Physics Series ◽

10.29167/a1i1p216-227 ◽

2018 ◽

Vol 1 (1) ◽

pp. 216-227

Author(s):

Rajitha Bakthula ◽

Suneeta Agarwal

Keyword(s):

Soft Tissues ◽

Singular Values ◽

Histogram Equalization ◽

Singular Value ◽

Background Suppression ◽

Edge Preservation ◽

Low Contrast ◽

X Ray ◽

X Ray Imaging ◽

Value Decomposition

Contrast enhancement is one of the important issues in Medical X-ray imaging since these image, in general, are of low contrast and luminance. In medical X-ray imaging system viewing the bone structure and soft tissues are important for better medical diagnosis. The accuracy of Medical diagnosis of a patient purely depends on the clarity of the image. Hence an X-ray image must be well enhanced at the same time edges must be preserved and highlighted while applying image pre-processing technique. This is a challenging task in literature. In literature many techniques had been proposed for improving the low contrast images in various applications like satellite images, medical images, etc. Standard methods include General Histogram Equalization (GHE), Local Histogram Equalization (LHE), AHE or CLACHE, Brightness Preserving Histogram Equalization (BBHE), etc. All these methods rely on histogram equalization on the entire image, might lead to loss of edge information. Since Soft-Tissues and bone pixels have similar values, global equalization methods might fail. So to resolve these challenges, this paper presents a new method using Singular Value Decomposition (SVD) for image enhancement and also improves the edge quality. Proposed method works in two phases: background suppression and foreground enhancement. The proposed method decomposes the x-ray image using SVD and extracts the singular values of the image (which represents the order of luminance in the image). These singular values are further analyzed to identify the highly dominating singular values and are used for background suppression. Later the foregrounds, i.e., the bone pixels are enhanced through histogram equalization. Advantage of the proposed method is shown experimentally using various images like a hand, pelvic, skull and chest of a human. As standard matrices, PSNR, SNR, and Entropy focus on complete enhanced image (i.e., foreground and background) might fail to justify the improvement in enhancement. Thus, in this paper performance is evaluated using standard texture metrics: homogeneity, contrast, entropy, mean and standard deviation. Results of the proposed method are compared with standard literature methods like AHE, CLACHE, MMBEBH, and BHE. The proposed method has shown the better results with highest homogeneity (0.88), lowest contrast (0.32), highest correlation (0.97), and highest energy (0.21). Edge preservation accuracy is also highest (i.e., 0.98%) in comparison to literature methods.

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