Conventional Histogram Equalization Methods Research Articles

Developing a Gaussian Model-based Histogram Equalization Technique for Enhancement of Breast Thermography Images

Purpose: Breast cancer is one of the most prevalent diseases among women worldwide. One of the effective ways to reduce the risk of death from breast cancer is early detection by breast screening methods such as thermography. Thermography is non-invasive infrared imaging that detects early symptoms of breast angiogenesis based on the temperature difference and asymmetric patterns between left and right breasts. For better visual perception, it is essential to increase the medical image quality and contrast.
 Materials and Methods: Histogram Equalization (HE) is a common and effective technique for contrast enhancement that uses the whole dynamic range of gray levels. In this paper, we propose to apply the equalization technique to the object part of the image rather than the background. One way is to use Otsu's method for automatic image thresholding. A more efficient approach to extract the body region is to fit a bimodal Gaussian distribution on the temperature information and restrict the equalization on gray level ranges corresponding to temperatures between the mean minus/plus three times of standard deviation.
 Results: We compared the performance of the proposed approach with six conventional HE methods by using objective criteria, including Absolute Mean Brightness Error (AMBE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSI), and Entropy.
 Conclusion: Based on objective measures, as well as subjective visual inspection of the results, the proposed Gaussian model-based HE has better performance in contrast enhancement and brightness preservation among other methods.

Image Contrast Enhancement by Stepwise Histogram Specification

We propose an algorithm for enhancing the contrast of grayscale images by stepwise histogram specification, which produces a contrast-enhanced image of which the histogram is stepwise piecewise constant, and has no gap between neighboring intensities, that is, no 0-valued bin exists in the histogram. As a result, false contouring effect caused by the intensity gap can be avoided successfully. In order to evaluate the performance of contrast enhancement methods, we also propose an image quality measure, which incorporates two conventional image quality measures into a single one. Experimental results show that the proposed algorithm outperforms two conventional histogram equalization methods in terms of the image quality measure.

Survey of Contrast Enhancement Techniques based on Histogram Equalization

This Contrast enhancement is frequently referred to as one of the most important issues in image processing. Histogram equalization (HE) is one of the common methods used for improving contrast in digital images. Histogram equalization (HE) has proved to be a simple and effective image contrast enhancement technique. However, the conventional histogram equalization methods usually result in excessive contrast enhancement, which causes the unnatural look and visual artifacts of the processed image. This paper presents a review of new forms of histogram for image contrast enhancement. The major difference among the methods in this family is the criteria used to divide the input histogram. Brightness preserving Bi-Histogram Equalization (BBHE) and Quantized Bi-Histogram Equalization (QBHE) use the average intensity value as their separating point. Dual Sub-Image Histogram Equalization (DSIHE) uses the median intensity value as the separating point. Minimum Mean Brightness Error Bi-HE (MMBEBHE) uses the separating point that produces the smallest Absolute Mean Brightness Error (AMBE). Recursive Mean-Separate Histogram Equalization (RMSHE) is another improvement of BBHE. The Brightness preserving dynamic histogram equalization (BPDHE) method is actually an extension to both MPHEBP and DHE. Weighting mean-separated sub-histogram equalization (WMSHE) method is to perform the effective contrast enhancement of the digital image.

Reducing scanning electron microscope charging by using exponential contrast stretching technique on post‐processing images

An exponential contrast stretching (ECS) technique is developed to reduce the charging effects on scanning electron microscope images. Compared to some of the conventional histogram equalization methods, such as bi-histogram equalization and recursive mean-separate histogram equalization, the proposed ECS method yields better image compensation. Diode sample chips with insulating and conductive surfaces are used as test samples to evaluate the efficiency of the developed algorithm. The algorithm is implemented in software with a frame grabber card, forming the front-end video capture element.

Image Quality Analysis of a Novel Histogram Equalization Method for Image Contrast Enhancement

The use of image contrast enhancement has become increasingly essential due to the need to better show the visual information contained within the image for all vision-based systems. This has lead to motivation for the design of a powerful and accurate automatic contrast enhancement for a digital image. Histogram equalization is the most commonly used contrast enhancement method. However, the conventional histogram equalization methods usually result in excessive contrast enhancement, which causes the unnatural look and visual artifacts of the processed image. In this paper, we propose a novel histogram equalization method using the automatic histogram separation along with the piecewise transformed function. The contrast enhancement results of the proposed method were not only analyzed through qualitative visual inspection and for quantitative accuracy, but are also compared to the results of other state-of-the-art methods.

Histogram Equalization Utilizing Window-Based Smoothed CDF Estimation for Feature Compensation

In this letter, we propose a new histogram equalization method to compensate for acoustic mismatches mainly caused by corruption of additive noise and channel distortion in speech recognition. The proposed method employs an improved test cumulative distribution function (CDF) by more accurately smoothing the conventional order statistics-based test CDF with the use of window functions for robust feature compensation. Experiments on the AURORA 2 framework confirmed that the proposed method is effective in compensating speech recognition features by reducing the averaged relative error by 13.12% over the order statistics-based conventional histogram equalization method and by 58.02% over the mel-cepstral-based features for the three test sets.

Compensating Acoustic Mismatch Using Class-Based Histogram Equalization for Robust Speech Recognition

A new class-based histogram equalization method is proposed for robust speech recognition. The proposed method aims at not only compensating for an acoustic mismatch between training and test environments but also reducing the two fundamental limitations of the conventional histogram equalization method, the discrepancy between the phonetic distributions of training and test speech data, and the nonmonotonic transformation caused by the acoustic mismatch. The algorithm employs multiple class-specific reference and test cumulative distribution functions, classifies noisy test features into their corresponding classes, and equalizes the features by using their corresponding class reference and test distributions. The minimum mean-square error log-spectral amplitude (MMSE-LSA)-based speech enhancement is added just prior to the baseline feature extraction to reduce the corruption by additive noise. The experiments on the Aurora2 database proved the effectiveness of the proposed method by reducing relative errors by over the mel-cepstral-based features and by over the conventional histogram equalization method, respectively.

Recursive sub-image histogram equalization applied to gray scale images

A novel recursive sub-image histogram equalization (RSIHE) is developed to overcome the drawbacks of generic histogram equalization (HE) for gray scale images. Compared to some of the conventional HE methods, such as bi-histogram equalization and recursive mean-separate histogram equalization, the proposed RSIHE method yields better image compensation. The scanning electron microscope images are used as test images to evaluate the efficiency of the developed algorithm. The algorithm is implemented in software with a frame grabber card, forming the front-end video capture element.

Class-Based Histogram Equalization for Robust Speech Recognition

A new class-based histogram equalization method is proposed for robust speech recognition. The proposed method aims at not only compensating the acoustic mismatch between training and test environments, but also at reducing the discrepancy between the phonetic distributions of training and test speech data. The algorithm utilizes multiple class-specific reference and test cumulative distribution functions, classifies the noisy test features into their corresponding classes, and equalizes the features by using their corresponding class-specific reference and test distributions. Experiments on the Aurora 2 database proved the effectiveness of the proposed method by reducing relative errors by 18.74%, 17.52%, and 23.45% over the conventional histogram equalization method and by 59.43%, 66.00%, and 50.50% over mel-cepstral-based features for test sets A, B, and C, respectively.