Iterated Adaptive Entropy-Clip Limit Histogram Equalization for Poor Contrast Images

Majeed, Samer Hameed; Isa, Nor Ashidi Mat

doi:10.1109/access.2020.3014453

Cited by 18 publications

(12 citation statements)

References 45 publications

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“…These techniques have been proven capable of preserving the details of certain regions in the image and reducing the dominance of the high-frequency effect of the histogram. High speed quantile based HE (HSQHE) [16], weighted [19], contrast limited adaptive HE (CLAHE) [20], iterated adaptive entropy clip limit HE (IAECHE) [21], and dynamic clipped HE DCLHE [10]. The AHE technique produces a resultant image with homogeneous brightness [19] and an unnatural appearance [21,22], is time consuming, and could not reduce the noise.…”

Section: Histogram Equalizationmentioning

confidence: 99%

“…High speed quantile based HE (HSQHE) [16], weighted [19], contrast limited adaptive HE (CLAHE) [20], iterated adaptive entropy clip limit HE (IAECHE) [21], and dynamic clipped HE DCLHE [10]. The AHE technique produces a resultant image with homogeneous brightness [19] and an unnatural appearance [21,22], is time consuming, and could not reduce the noise. The CLAHE technique divides the input image into small blocks along with set a value to clip the histogram to enhance the tiny details and improve the contrast of the image.…”

Section: Histogram Equalizationmentioning

confidence: 99%

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Dynamic Clip Limit Window Size Histogram Equalization for Poor Information Images

2022

IJIES

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During the image enhancement process, hidden information, poor capturing device quality lead to poor image contrast, insufficient user experience, and an inappropriate data collecting environment setting have all been noted as serious concerns. Histogram equalization techniques have been used to solve the challenges described above. Nonetheless, the images obtained using these methods are frequently impacted by unwanted artifacts, and unnatural appearances effects. Due to that, this research presented a novel strategy for enhancing contrast called Dynamic Clip Limit Window Size Histogram Equalization. The proposed technique uses a new fitness function of combining the discrete entropy and root mean square error parameters. The proposed strategy's qualitative and quantitative result is validated and assessed against 5 state-of-the-art strategies (HSQHE, ACLTSHE, CLAHE, AEIHE, and IAECHE). The proposed technique proved its high capability to produce the best average DE, CII, and SSI values for the Faces-199 dataset (7.869, 1.000, and 0.999, respectively).

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Section: Histogram Equalizationmentioning

confidence: 99%

Section: Histogram Equalizationmentioning

confidence: 99%

Dynamic Clip Limit Window Size Histogram Equalization for Poor Information Images

2022

IJIES

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“…(11) (12) The MVSIHE algorithm finds the maximum value of the variance σ 2 . After finding optimum threshold kopt (or kH2), same procedure from Equation 9to Equation 12 is repeated for two distinct histograms.…”

Section: Histogram Segmentationmentioning

confidence: 99%

“…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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“…The Iterated Adaptive Entropy Clip Limit HE (IAECHE) separates an image into multiple sub-images and uses an image histogram to determine the clip limit (CL) value by identifying the peaks from these sub-images [41]. The IAECHE calculates the best CL value in an iterative and adaptive process.…”

Section: B) Histogram-based Divisionmentioning

confidence: 99%

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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Iterated Adaptive Entropy-Clip Limit Histogram Equalization for Poor Contrast Images

Cited by 18 publications

References 45 publications

Dynamic Clip Limit Window Size Histogram Equalization for Poor Information Images

Dynamic Clip Limit Window Size Histogram Equalization for Poor Information Images

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

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

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