An approach for de-noising and contrast enhancement of retinal fundus image using CLAHE

Bhardwaj, Sonali; Sahu, Sima; Singh, Amit Kumar; Ghrera, Satya Prakash; Elhoseny, Mohamed

doi:10.1016/j.optlastec.2018.06.061

Cited by 216 publications

(72 citation statements)

References 19 publications

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“…Later, Kansal and Tripathi [41] also introduced an adaptive gamma correction in which they change the value of gamma for each image. In another simple method [39], the authors combined CLAHE with different filters in order to enhance the image contrast and also to reduce the noise. Vishwakarma and Goel [40] used CLAHE along with logarithm transform for image contrast enhancement.…”

Section: Methodsmentioning

confidence: 99%

An Adaptive Image Contrast Enhancement Technique for Low-Contrast Images

et al. 2019

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Contrast enhancement is important and plays vital role in many applications. Histogram equalization-based techniques are widely used techniques for contrast enhancement. However, it faces the contrast over-stretching, which in return causes the loss of details and unnatural look to the target image. To address this issue, this work presents a novel scheme for image contrast enhancement. The contribution of the proposed scheme is twofold. First, the image can lose many important information when an image size is decreased. For that, the image is transformed from spatial to wavelet domain so that the multi-resolution can be achieved. Second, Gamma correction is a proven technique that produces natural look and preserves mean brightness of an image with the choice of optimal gamma values. Here, Particle Swarm Optimization (PSO) is utilized to select the optimal gamma values. In this study, an effective fitness function is proposed to maximize the performance of PSO. Experimental findings show that the proposed approach improve the image contrast up to a greater extent without introducing any artifacts.

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Section: Methodsmentioning

confidence: 99%

An Adaptive Image Contrast Enhancement Technique for Low-Contrast Images

et al. 2019

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“…The Peak signal-to-noise ratio (PSNR) is the most commonly used objective metric for evaluating image quality [49], [50]. It is also one of the most popular and reliable performance metrics for image enhancement and other fields [51]. The larger the PSNR value between the two images, the more similar the images are.…”

Section: B Performance Comparisonmentioning

confidence: 99%

Global and Adaptive Contrast Enhancement for Low Illumination Gray Images

Liu

et al. 2019

IEEE Access

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Aiming at solving the problems of overall darkness, uneven illumination and low contrast of image under low illumination conditions, we present a global and adaptive contrast enhancement algorithm for low illumination gray images in this paper. The proposed algorithm is based on the Bilateral Gamma Adjustment function and combined with the particle swarm optimization (PSO). For the PSO, the gray standard variance is integrated into the evaluation function. To reconcile the dilemma of promoting the gray values of dark areas and suppressing the gray values of local bright areas at the same time, the information of entropy, edge content, and gray standard variance are used as the objective function for each particle to evaluate the gray image enhancement results. Then, the algorithm globally enhances the quality of the image by determining the optimal α value. Meanwhile, the learning factors of the PSO are updated during the iteration of optimization in the proposed algorithm. Compared with histogram equalization (HE), double plateau histogram equalization (DPHE), contrast limited adaptive histogram equalization (CLAHE), linear contrast stretching (LCS), adaptive gamma correction weighting distribution (AGCWD), the traditional PSO and MSF-PSO algorithm, the proposed algorithm significantly enhances the visual effect of the low illumination gray images. The experimental results demonstrate the superior capabilities of the proposed algorithm in enhancing the contrast of the image, such as improving the overall visual effect of the low illumination gray image and avoiding over-enhancement in the local area (s). INDEX TERMS Low luminance images, bilateral gamma adjustment, uneven illumination, particle swarm optimization.

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“…The main reason for image quality is due to the decrease in contrast and brightness. Early image enhancement was enhanced by histogram equalization [8].…”

Section: Introductionmentioning

confidence: 99%

Dehaze Enhancement Algorithm Based on Retinex Theory for Aerial Images Combined with Dark Channel

Liu¹,

Liu²,

Lan³

et al. 2020

OALib

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In order to improve the visual effect of aerial images in foggy weather, a Retinex defogging algorithm combining dark channel priors is proposed to solve the phenomenon of insufficient defogging and over-enhancement of aerial defogging in Reinex defogging. First, the foggy original image is decomposed into foggy incident light components and foggy reflected light components by Retinex theory. Then, the principle of foggy image degradation is analyzed by an atmospheric scattering model, and the dark channel prior defogging algorithm is used to obtain the incident light component and reflected light component after defogging. Finally, a fog-free image was restored through the Reitnex model. The effectiveness of this algorithm is verified through experiments. By comparing and analyzing this algorithm with other defogging algorithms, this algorithm has higher contrast and color fidelity.

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An approach for de-noising and contrast enhancement of retinal fundus image using CLAHE

Cited by 216 publications

References 19 publications

An Adaptive Image Contrast Enhancement Technique for Low-Contrast Images

An Adaptive Image Contrast Enhancement Technique for Low-Contrast Images

Global and Adaptive Contrast Enhancement for Low Illumination Gray Images

Dehaze Enhancement Algorithm Based on Retinex Theory for Aerial Images Combined with Dark Channel

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