Contrast enhancement via texture region based histogram equalization

Singh, Kulbir; Vishwakarma, Dinesh Kumar; Walia, Gurjit Singh; Kapoor, Rajiv

doi:10.1080/09500340.2016.1154194

Cited by 102 publications

(43 citation statements)

References 17 publications

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“…In this section, the images from the databases http://brainweb.bic.mni.mcgill.ca/brainweb/, http://ctisus.com and http://radpod.org are tested to demonstrate the effectiveness of the proposed technique. We compare the performance of the proposed method with several other state‐of‐the‐art approaches, namely HE, BPDFHE, general framework based on HE for image contrast enhancement (WAHE), contextual and variational contrast enhancement (CVC), layered difference representation (LDR), dominant orientation‐based texture HE (DOTHE) and edge‐based texture HE (ETHE) . The objective as well as subjective assessment are selected to demonstrate the enhanced effect.…”

Section: Resultsmentioning

confidence: 99%

“…We compare the performance of the proposed method with several other state-of-the-art approaches, namely HE, 24 BPDFHE, 25 general framework based on HE for image contrast enhancement (WAHE), 26 contextual and variational contrast enhancement (CVC), 27 layered difference representation (LDR), 28 dominant orientation-based texture HE (DOTHE) 29 and edge-based texture HE (ETHE). 29 The objective as well as subjective assessment are selected to demonstrate the enhanced effect. In terms of the objective assessment, the entropy (H), 29 average local contrast (LC), 30 spatial frequency (SF), 30 mean gradient (MG) 30 are used.…”

Section: Res U Lts An D Discussionsmentioning

confidence: 99%

“…29 The objective as well as subjective assessment are selected to demonstrate the enhanced effect. In terms of the objective assessment, the entropy (H), 29 average local contrast (LC), 30 spatial frequency (SF), 30 mean gradient (MG) 30 are used. Larger these metrics represent better image enhancement.…”

Section: Res U Lts An D Discussionsmentioning

confidence: 99%

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A novel brain image enhancement method based on nonsubsampled contourlet transform

Jia

2017

Int J Imaging Syst Tech

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In this article, a novel brain image enhancement approach based on nonsubsampled contourlet transform (NSCT) is proposed. First, the image is decomposed into a low‐frequency component and several high‐frequency components by the NSCT; Second, the gamma correction is applied to deal with the low‐frequency sub‐band coefficients, and the adaptive threshold is used to remove the noise of the high‐frequency sub‐bands coefficients; Third, the inverse nonsubsampled contourlet transform is adopted to reconstruct the processed coefficients; Finally, the unsharp filter is used to enhance the reconstructed image. The experimental results demonstrate that the performance of the proposed method is superior to the state‐of‐the‐art algorithms in terms of brain image enhancement.

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Section: Resultsmentioning

confidence: 99%

Section: Res U Lts An D Discussionsmentioning

confidence: 99%

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A novel brain image enhancement method based on nonsubsampled contourlet transform

Jia

2017

Int J Imaging Syst Tech

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“…The approaches, including the traditional histogram equalization method (HE), 27 contrast limited adaptive histogram equalization method (CLAHE), 28 the image enhancement algorithm based on nonsubsampled shearlet transform and guided filtering (NSST-GF), 20 the edge-based texture histogram equalization method (ETHE), 29 the dominant orientation-based texture histogram equalization method (DOTHE), 29 the feature-linking model for image enhancement (FLM), 30 the linking synaptic computation network for image enhancement (LSCN), 31 are applied in this section as the comparison techniques. In this paper, the decomposition layer of NSST is 3, and the directions are 8, 16, 16 in scales from coarser to finer, respectively; the γ is set to 1.2 and the c is set to 0.5.…”

Section: Resultsmentioning

confidence: 99%

“…In this paper, the decomposition layer of NSST is 3, and the directions are 8, 16, 16 in scales from coarser to finer, respectively; the γ is set to 1.2 and the c is set to 0.5. In order to evaluate the enhanced results of different approaches objectively, four metrics are used to quantify the enhanced image, i.e., entropy (H), 29 local contrast (LC), 31 spatial frequency (SF), 31 and average gradient (AG). 31 To a…”

Section: Resultsmentioning

confidence: 99%

Microscopy mineral image enhancement based on improved adaptive threshold in nonsubsampled shearlet transform domain

Jia

2018

AIP Advances

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In this paper, a novel microscopy mineral image enhancement method based on adaptive threshold in non-subsampled shearlet transform (NSST) domain is proposed. First, the image is decomposed into one low-frequency sub-band and several high-frequency sub-bands. Second, the gamma correction is applied to process the low-frequency sub-band coefficients, and the improved adaptive threshold is adopted to suppress the noise of the high-frequency sub-bands coefficients. Third, the processed coefficients are reconstructed with the inverse NSST. Finally, the unsharp filter is used to enhance the details of the reconstructed image. Experimental results on various microscopy mineral images demonstrated that the proposed approach has a better enhancement effect in terms of objective metric and subjective metric.

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Cuckoo search optimization‐based image color and detail enhancement for contrast distorted images

Subramani¹,

Veluchamy²

2022

Color Research & Application

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Researchers have developed various contrast enhancement techniques to provide perceptually good quality images for many vision‐based applications. An exposure‐based optimally weighted gamma correction framework is proposed in this work to overcome the drawback of low contrast and unclear detail in contrast distorted image. At first, the intensities of the input image are expanded and compressed to increase the fine details and reduce the viewable artifacts. The gamma correction and weighted sum approach are then introduced to provide good contrast and natural‐looking images with optimal gamma values. Besides, the cuckoo search algorithm is employed to calculate the optimum value of the gamma function. In this work, the mean squared error is used as the objective function to maximize the information content of the enhanced image. The simulation results reveal the efficiency of the proposed method in enhancing the image for various levels of contrast distortion. Besides, quantitative and qualitative comparisons with the recently published methods demonstrate the superiority of the proposed algorithm in improving details and natural color of the contrast distorted images.

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Contrast enhancement via texture region based histogram equalization

Cited by 102 publications

References 17 publications

A novel brain image enhancement method based on nonsubsampled contourlet transform

A novel brain image enhancement method based on nonsubsampled contourlet transform

Microscopy mineral image enhancement based on improved adaptive threshold in nonsubsampled shearlet transform domain

Cuckoo search optimization‐based image color and detail enhancement for contrast distorted images

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