Histogram Equalization: A Strong Technique for Image Enhancement

Singh, Ravindra Pal; Dixit, Manish

doi:10.14257/ijsip.2015.8.8.35

Cited by 48 publications

(20 citation statements)

References 5 publications

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“…Fig.3 shows a diagram of dependence of IPI on a number of the frame. Numbers of frames are marked in horizontal direction and values of quality indices are marked in vertical direction, A0-A3 -contrasting algorithms (A0 -without contrasting, A1 -linear stretch of the histogram [5], A2 -histogram equalization [6], A3 -Multi Scale Retinex with Color Restoration [7]), Auto -IPI values under automatic selection of contrasting algorithms. On the diagram we can see areas which value of IPI has not the best value for, it is described by delay occurred because the stack is used and evaluation is performed after every t=50 frames.…”

Section: Interpolation Methods Of Proportional Application Of Two Bounmentioning

confidence: 99%

Automatic adjustment of image processing pipeline

Kolchaev¹,

Muratov²,

Nikiforov³

2017

Collection of Selected Papers of the III International Conference on Information Technology and Nanotechnology

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Mathematical processing of images in real-time vision systems can be conventionally divided into two stages: preprocessing (filtering, contrasting, protection from natural distortions, etc.) and final one (imposition, visualization, solution of the navigation task, etc.). Mentioned tasks can be solved by a lot of known and specially developed methods with various degrees of efficiency. The present paper suggests a mathematical criterion model and algorithm of automatic selection of the most effective method at each stage of the image processing pipeline in relation to the current situation at its input.

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Section: Interpolation Methods Of Proportional Application Of Two Bounmentioning

confidence: 99%

Automatic adjustment of image processing pipeline

Kolchaev¹,

Muratov²,

Nikiforov³

2017

Collection of Selected Papers of the III International Conference on Information Technology and Nanotechnology

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show abstract

“…More details of the histogram equalization are referred from the study by Singh and Dixit. [ 21 ] After equalizing the histogram, the obtained image is multiplied again in the parameter Max Luminosity defined in Eq. 5 to keep the color intensities in the range of 0–100.…”

Section: Proposed Algorithmmentioning

confidence: 99%

Employing the local radon transform for melanoma segmentation in dermoscopic images

et al. 2018

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In recent years, the number of patients suffering from melanoma, as the deadliest type of skin cancer, has grown significantly in the world. The most common technique to observe and diagnosis of such cancer is the use of noninvasive dermoscope lens. Since this approach is based on the expert ocular inference, early stage of melanoma diagnosis is a difficult task for dermatologist. The main purpose of this article is to introduce an efficient algorithm to analyze the dermoscopic images. The proposed algorithm consists of four stages including converting the image color space from the RGB to CIE, adjusting the color space by applying the combined histogram equalization and the Otsu thresholding-based approach, border extraction of the lesion through the local Radon transform, and recognizing the melanoma and nonmelanoma lesions employing the ABCD rule. Simulation results in the designed user-friendly software package environment confirmed that the proposed algorithm has the higher quantities of accuracy, sensitivity, and approximation correlation in comparison with the other state-of-the-art methods. These values are obtained 98.81 (98.92), 94.85 (89.51), and 90.99 (86.06) for melanoma (nonmelanoma) lesions, respectively.

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“…This is necessary when the image is transformed into new image the gray probability distribution is uniform during gray transformation [21]. This model achieves this task proficiently by distribution out the most continuous intensity values.…”

Section: Preprocessing: Histogram Equalizationmentioning

confidence: 99%

Optimal key based homomorphic encryption for color image security aid of ant lion optimization algorithm

Shankar¹,

S.K²

2018

IJET

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The security of digital images is a basic and difficult task on the shared communication channel. Different strategies are utilized to secure the digital image, for example, encryption, steganography and watermarking. These are the techniques for the security of digital image to accomplish security objectives, i.e. secrecy, trustworthiness, and accessibility. In the proposed study, Homomorphic Encryption (HE) with optimal key selection for image security is utilized. Here the histogram equalization is introduced for altering image intensities to improve contrast. The histogram of an image generally speaks to the comparative frequency of occurrence of the different gray levels in the image. To increase the security level inspired Ant Lion Optimization (ALO) is considered, where the fitness function as max entropy the best-encrypted image is characterized as the image with most astounding entropy among adjacent pixels. Analyzing the outcomes from the performed experimental outcomes can accomplish abnormal state and great strength of proposed model compared with other encryption strategies.

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Histogram Equalization: A Strong Technique for Image Enhancement

Cited by 48 publications

References 5 publications

Automatic adjustment of image processing pipeline

Automatic adjustment of image processing pipeline

Employing the local radon transform for melanoma segmentation in dermoscopic images

Optimal key based homomorphic encryption for color image security aid of ant lion optimization algorithm

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