Image contrast enhancement using geometric mean filter

Hiary, Hazem; Zaghloul, Rawan; Al-Adwan, Aryaf; Al-Zoubi, Moh'd Belal

doi:10.1007/s11760-016-1029-8

Cited by 17 publications

(7 citation statements)

References 20 publications

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“…However, there may be some phenomena such as missing information and false contours, when the foggy density in the image is unbalanced, and the scene depth information is unknown. Histogram equalization of mean filtering [31] could enhance the image contrast, but could not bring enhanced details and remove noise effectively. In addition, a phenomenon of overexposure existed.…”

Section: Introductionmentioning

confidence: 99%

“…It has become the current mainstream method, due to the reason that it can be separated from the physical device. The goal of the image enhancement methods are used to improve contrast of image, including histogram equalization [28]- [30], smooth filter [31]- [34], and retinex enhancement [35]- [38]. Histogram equalization [28]- [30] could increase the dynamic range and improve the global contrast of the image.…”

Section: Introductionmentioning

confidence: 99%

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Retinex-Based Laplacian Pyramid Method for Image Defogging

et al. 2019

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To address the problem that the retinex algorithm cannot effectively enhance color and detail simultaneously, we propose a retinex-based laplacian pyramid method for image defogging. The method is implemented via MSRCR and laplacian pyramid, and it doesn't require additional hardware devices. The overall defogging process is composed of three vital parts: illumination color enhancement, detail of reflection component enhancement, and linear weighted fusion. Firstly, we add the gamma correction illumination back to reflection to achieve color enhancement. And then, the detail enhancement is achieved by the laplacian pyramid to process the reflection component. Finally, the detail enhanced image and color corrected image are used to reconstruct the clear image. Comparing with the state-of-the-art image defogging methods, the experimental results have shown that the contrast of defogged images can be effectively improved by the proposed method, and with better effects on both subjective and objective results. The contrast of images can be enhanced by the proposed method. Meanwhile, sufficient details can be preserved. Therefore, the proposed method is an effective image defogging method.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Retinex-Based Laplacian Pyramid Method for Image Defogging

et al. 2019

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“…Image enhancement consists of image quality improvement processes, allowing a better visual and computational analysis [1]. It is widely used in several applications due to its capability to overcome some of the limitations presented by image acquisition systems [2]. Deblurring, noise removal, and contrast enhancement are some examples of image enhancement operations.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, the main drawback in CLAHE, as reported in [26,27], is an improper hyperparameter selection that leads to decrease in image quality. The authors also state that the quality of the enhanced image depends most on the CL hyperparameter.…”

Section: Introductionmentioning

confidence: 99%

Machine learning hyperparameter selection for Contrast Limited Adaptive Histogram Equalization

Campos

Mastelini

Aguiar

et al. 2019

J Image Video Proc.

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Contrast enhancement algorithms have been evolved through last decades to meet the requirement of its objectives. Actually, there are two main objectives while enhancing the contrast of an image: (i) improve its appearance for visual interpretation and (ii) facilitate/increase the performance of subsequent tasks (e.g., image analysis, object detection, and image segmentation). Most of the contrast enhancement techniques are based on histogram modifications, which can be performed globally or locally. The Contrast Limited Adaptive Histogram Equalization (CLAHE) is a method which can overcome the limitations of global approaches by performing local contrast enhancement. However, this method relies on two essential hyperparameters: the number of tiles and the clip limit. An improper hyperparameter selection may heavily decrease the image quality toward its degradation. Considering the lack of methods to efficiently determine these hyperparameters, this article presents a learning-based hyperparameter selection method for the CLAHE technique. The proposed supervised method was built and evaluated using contrast distortions from well-known image quality assessment datasets. Also, we introduce a more challenging dataset containing over 6200 images with a large range of contrast and intensity variations. The results show the efficiency of the proposed approach in predicting CLAHE hyperparameters with up to 0.014 RMSE and 0.935 R 2 values. Also, our method overcomes both experimented baselines by enhancing image contrast while keeping its natural aspect.

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“…Mean filter is used to reduce noise in the image process, and it is an intuitive and simple method to make the image smooth [6]. Its principle is reducing the amount of intensity variation on pixel and the next [7]. Mean filter is based on the template for image convolution operation to achieve smooth processing [8].…”

Section: Introductionmentioning

confidence: 99%

Preservation of image edge feature based on snowfall model smoothing filter

Fan

Zhu

2018

J Image Video Proc.

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This paper proposed a snowfall model as a novel smoothing filter. The pixel composition of the image was similar to the geographic features, so it could be smooth because of snow accumulation. In the snowfall processing, luminance changes are linked to terrain and snowfall amount. Curvature and luminance gradient decided the amount of snowfall; the amount of snowfall became large on the parts where the curvature was large, and it became little on the parts where the gradient was steep. Snowfall algorithm simulates the natural snowfall process, which nonlinear diffusion and the target feature could be preserved well. Snowfall model has the same function as the Gaussian filter. The number of regions was reduced after Gaussian filter and snowfall model smoothing, respectively. The contrast experiment was carried out based on Watershed algorithm. The image area segmentation that pretreated through snowfall model was compared with Gaussian filter smoothing. The experimental result showed that the proposed snowfall model was a smoothing filter. It was able to realize edge preservation, which was the original purpose, and it was also possible to apply to region segmentation.

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Image contrast enhancement using geometric mean filter

Cited by 17 publications

References 20 publications

Retinex-Based Laplacian Pyramid Method for Image Defogging

Retinex-Based Laplacian Pyramid Method for Image Defogging

Machine learning hyperparameter selection for Contrast Limited Adaptive Histogram Equalization

Preservation of image edge feature based on snowfall model smoothing filter

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