Adaptive Entropy Index Histogram Equalization for Poor Contrast Images

Majeed, Samer Hameed; Isa, Nor Ashidi Mat

doi:10.1109/access.2020.3048148

Cited by 22 publications

(30 citation statements)

References 59 publications

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“…The nature-inspired metaheuristic approaches have been efficiently and successfully used to solve or mitigate the HE-based image enhancement issues such as the subjective effects of manual parameters setting and over-or under-enhancement. In this context, several natureinspired metaheuristic approaches have been hybridized with HE to address intensity inhomogeneity and to improve image contrast, as reported in [48], [49], [50], [51], [52], [53], [54], [55], [56], and [57].…”

Section: Metaheuristic-based Histogram Equalizationmentioning

confidence: 99%

“…In a recent study, the CLAHE approach combined with a whale optimization method termed AIEHE was proposed by [57] for poor contrast images enhancement. The AIEHE approach separates the input image into three sub-images.…”

Section: Metaheuristic-based Histogram Equalizationmentioning

confidence: 99%

“…A more recent study by [57] investigated an approach for enhancing retinal fundus images using the hybridized modified PSO with the CLAHE (MPSO-CLAHE) approach.…”

Section: Metaheuristic-based Histogram Equalizationmentioning

confidence: 99%

“…In the second stage, the Whale Optimization Algorithm (WOA) is applied to achieve the optimum CL (OptimumCL) value. The WOA is a metaheuristic optimization algorithm that simulates the food source chasing of the humpback whale by spreading bubbles net around the food source as a hunting strategy (refer to [57] for more details about WOA). In the final stage, two values (OptimumTS & OptimumCL) are applied as input parameters to the standard CLAHE in yielding the enhanced image.…”

Section: Contrast Limited Adaptive Histogram Equalizationmentioning

confidence: 99%

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Adaptive Clip Limit Tile Size Histogram Equalization for Non-Homogenized Intensity Images

Fawzi¹,

Achuthan

Belaton

2021

IEEE Access

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Intensity inhomogeneity, hidden details, poor image contrast due to low capturing device quality, limited user experience, and inappropriate environment setting during data acquisition are major issues reported during the image enhancement process. Histogram Equalization (HE) approaches have been commonly deployed to overcome the above-listed problems, apart from improving image contrast. Nevertheless, the resulting images retrieved after undergoing these approaches are often affected by undesired artifacts, unnatural looks, and unpleasant washed-out effects. As such, this study introduces a new approach called Adaptive Clip Limit Tile Size Histogram Equalization (ACLTSHE). The ACLTSHE initially assigns the optimum clip limit (CL) and tile size minimum or maximum values. Then, a new fitness function called DataSignal is deployed to produce a set of non-dominated solutions by adaptively computing the optimum CL value. The performance of the proposed ACLTSHE approach was assessed and compared with conventional Clip Limit HE (CLHE) and several state-of-the-art approaches, such as Dynamic Clipped HE (DCLHE), Iterated Adaptive Entropy Clip Limit HE (IAECHE), Mean and Variance Sub-image HE (MVSIHE), and Adaptive Entropy Index HE (AEIHE). The outcomes were assessed both qualitatively and quantitatively by using six evaluation metrics, Discrete Entropy (DE), Absolute Mean Brightness Error (AMBE), Peak Signalto-Noise Ratio (PSNR), Contrast Improvement Index (CII), Root Mean Square Error (RMSE), Structure Similarity Index (SSI) and Standard Deviation (SD). The quantitative evaluation of three dataset images (Pasadena-houses 2000, faces 1999, and BraTS 2019) verified that the proposed approach outperformed the compared approaches in terms of improved DE, enhanced contrast, and highlighted local details without losing the original image structures. INDEX TERMS Histogram equalization-based technique, histogram entropy, histogram clip limit, tile size.

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Section: Metaheuristic-based Histogram Equalizationmentioning

confidence: 99%

Section: Metaheuristic-based Histogram Equalizationmentioning

confidence: 99%

“…A more recent study by [57] investigated an approach for enhancing retinal fundus images using the hybridized modified PSO with the CLAHE (MPSO-CLAHE) approach.…”

Section: Metaheuristic-based Histogram Equalizationmentioning

confidence: 99%

Section: Contrast Limited Adaptive Histogram Equalizationmentioning

confidence: 99%

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Adaptive Clip Limit Tile Size Histogram Equalization for Non-Homogenized Intensity Images

Fawzi¹,

Achuthan

Belaton

2021

IEEE Access

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“…Algorithms like HE, BBHE, DSIHE, RSIHE and ESIHE suffers from different artifacts that occur in the output image but MVSIHE is one of the most outstanding methods among other HE techniques according to [11], [12]. Although it has a relatively good performance, its performance mainly depends on the delta parameter, which controls the rate of the image fusing [10].…”

Section: Introductionmentioning

confidence: 99%

Sub-Image Histogram Equalization using Coot Optimization Algorithm for Segmentation and Parameter Selection

Kuran¹,

Kuran²,

Er³

2022

Artificial Intelligence and Applications

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Contrast enhancement is very important in terms of assessing images in an objective way. Contrast enhancement is also significant for various algorithms including supervised and unsupervised algorithms for accurate classification of samples. Some contrast enhancement algorithms solve this problem by addressing the low contrast issue. Mean and variance based sub-image histogram equalization (MVSIHE) algorithm is one of these contrast enhancements methods proposed in the literature. It has different parameters which need to be tuned in order to achieve optimum results. With this motivation, in this study, we employed one of the most recent optimization algorithms, namely, coot optimization algorithm (COA) for selecting appropriate parameters for the MVSIHE algorithm. Blind/referenceless image spatial quality evaluator (BRISQUE) and natural image quality evaluator (NIQE) metrics are used for evaluating fitness of the coot swarm population. The results show that the proposed method can be used in the field of biomedical image processing.

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Photovoltaic Module Fault. Part 1: Detection with Image Processing Approaches

Kurukuru

Haque

2022

Fault Analysis and Its Impact on Grid‐connected Photovoltaic Systems Performance

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Adaptive Entropy Index Histogram Equalization for Poor Contrast Images

Cited by 22 publications

References 59 publications

Adaptive Clip Limit Tile Size Histogram Equalization for Non-Homogenized Intensity Images

Adaptive Clip Limit Tile Size Histogram Equalization for Non-Homogenized Intensity Images

Sub-Image Histogram Equalization using Coot Optimization Algorithm for Segmentation and Parameter Selection

Photovoltaic Module Fault. Part 1: Detection with Image Processing Approaches

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