Contrast Enhancement Using Dominant Brightness Level Analysis and Adaptive Intensity Transformation for Remote Sensing Images

Blood vessel images can provide considerable information of many diseases, which are widely used by ophthalmologists for disease diagnosis and surgical planning. In this paper, we propose a novel method for the blood Vessel Enhancement via Multi-dictionary and SparseCoding (VE-MSC). In the proposed method, two dictionaries are utilized to gain the vascular structures and details, including the Representation Dictionary (RD) generated from the original vascular images and the Enhancement Dictionary (ED) extracted from the corresponding label images. The sparse coding technology is utilized to represent the original target vessel image with RD. After that, the enhanced target vessel image can be reconstructed using the obtained sparse coefficients and ED. The proposed method has been evaluated for the retinal vessel enhancement on the DRIVE and STARE databases. Experimental results indicate that the proposed method can not only effectively improve the image contrast but also enhance the retinal vascular structures and details.

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“…[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] and STARE as the evaluating data. Fig.…”

Section: Enhancement Resultsunclassified

“…(2) Transformation-based method transfers the image to other space [18][19][20][21], where the blood vessel can be enhanced. Miao et al [22] proposed a retinal vessel enhancement algorithm based on multi-scale top-hat transformation and histogram fitting stretching.…”

Section: Related Workmentioning

confidence: 99%

Blood vessel enhancement via multi-dictionary and sparse coding: Application to retinal vessel enhancing

Chen

Shao

et al. 2016

Neurocomputing

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“…Other hand, the procedure enhance the local contrast of images. Reshma lakshmi et.all(2013) [10] has suggested a novel contrast improvement algorithm. Disadvantages of surviving contrast improvement procedures are amended by a scientific software called 'Fuzzy set'.…”

Section: Mean Brightness Preserving Histogram Equalization (Mbphe)mentioning

confidence: 99%

Study on Various Techniques of Image Enhancement

Kaur¹,

Kumar²

2017

IJCA

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This paper has discuss the various techniques for image enhancement i.e histogram equalization, Brightness preserving bi-histogram equalization(BBHE), Dualistic SubImage Histogram Equalization (DSIHE), Minimum Mean Brightness Error Bi-HE Method (MMBEBHE), Recursive Mean -Separate HE Method (RMSHE),Mean brightness preserving histogram equalization(MBPHE).As well as it represents the comparison between the various techniques that shows the image enhances the overall contrast and visibility of local details. The review has shown that contrast enhancement approach based on dominant brightness level analysis and adaptive intensity transformation for remote sensing images. General TermsEnhance the contrast of images,comparison of different techniques of image enhancement.

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“…Separating the mean before performing histogram equalization provides better contrast enhancement with brightness preservation [15,22].Chen et al proposed RMSHE in which image is separated into two sub images based on the mean of original image. After separating the mean, the histogram of the two…”

Section: Recursive Mean-separate Histogram Equalization (Rmshe)mentioning

confidence: 99%

Mammographic Image Enhancement Using Indirect Contrast Enhancement Techniques – A Comparative Study

Akila¹,

Jayashree²,

Vasuki³

2015

Procedia Computer Science

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Contrast enhancement is an important issue in the field of mammographic image processing. It can be classified into two categories: direct contrast enhancement and in direct contrast enhancement. Indirect contrast enhancement involves in modifying histogram of the image. Histogram equalization (HE) is the simplest indirect contrast enhancement technique which is widely used for contrast enhancement. Many variants of HE are proposed so far. Comparison of these techniques is significantly essential in deciding a suitable algorithm for enhancement and further processing. In this paper we applied few indirect contrast enhancement techniques namely histogram equalization, CLAHE, BBHE, RMSHE, MMBEBHE to preprocess the mammographic images. The performance of the methods is measured using effective measure of enhancement (EME) and peak signal to noise ratio (PSNR).

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Contrast Enhancement Using Dominant Brightness Level Analysis and Adaptive Intensity Transformation for Remote Sensing Images

Cited by 80 publications

References 13 publications

Blood vessel enhancement via multi-dictionary and sparse coding: Application to retinal vessel enhancing

Blood vessel enhancement via multi-dictionary and sparse coding: Application to retinal vessel enhancing

Study on Various Techniques of Image Enhancement

Mammographic Image Enhancement Using Indirect Contrast Enhancement Techniques – A Comparative Study

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