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Histogram Equalization (HE) is a simple and effective image enhancement technique.But, it tends to change the mean brightness of the image to the middle level of the permitted range, and hence is not a very suitable for consumer product. While preserving the original brightness is essential to avoid annoying artefacts. To preserve brightness and to enhance contrast of images, numerous methods are introduced, but many of them present unwanted artefacts such as intensity saturation, over-enhancement and noise amplification. In the present paper, available histogram equalization based methods are reviewed and compared with image quality measurement (IQM)tools such as Absolute Mean Brightness Error (AMBE) to assess brightness preserving and Peak Signal-to-Noise Ratio (PSNR) to evaluate contrast enhancement.

According to the color difference fabric dyeing measurement in production, partial color existed bad real-time problems. Color difference and color of RGB fabric images put forward by using the method of color distribution measurement of general, which needed rapid measurement of large area color. According to RGB distribution histogram of the image, the measured color with sample color similarity and distribution curve of characteristic value was calculated, obtained the RGB color model, and gives a method of a partial color judging. The experiment proves the feasibility of this measurement fabric color difference and the judgment method and effectiveness of partial color. Color cast detection method has overcome the limitations of the traditional methods of detecting image color cast. In order to partial color image detection is performed after correction, using a combination of gray world and a perfect reflection of the color correction method, to make up for the shortcomings of traditional method. The experimental results show that, the color deviation correction method of image analysis based on detection improved the reliability of color cast detection. The method uses characteristics of image analysis, it has universal applicability.

Color Image enhancement is a process in which the perceptual information of an image is improved to obtain more information and details contained in the image. It improves the subjective quality of an image by working with original data. This paper focuses on evaluating the performance of various image enhancement techniques. These techniques are either based on histogram modification or are based on fuzzy logic. The techniques are compared using two quantitative measures namely; Contrast Improvement index (CII) and Tenengrad measure. The results have shown that Lab and edge preservation based fuzzy image enhancement (LEFM) yields the best results.

In this paper, we propose a fuzzy based histogram equalization method. We adopt Kaufmann’s measure of fuzziness concept for histogram equalization. The main idea of this paper is to use and assess fuzziness. The proposed histogram equalization method is applied to Y channel of YUV, which is transformed signal from RGB image. The obtained and improved Y channel is rejoined to the other I and Q signals and retransformed to RGB image. The objective and the visual performance are compared in simulation results section.

The contrast of an image is a feature which determines how image looks better visually. In this paper, we are analysing the capability of activation functions for contrast enhancement. Activation functions are classically used in neural network. In this paper, Activation function creates a mask which is operated on the image on pixel by pixel basis. On the basis of activation function the pixel value of image is changed which improves the contrast of image. We have used various activation functions such as sigmoid function, bipolar sigmoid function, RAMP function, hyperbolic tangent function. Contrast enhancement using these activation functions has been successfully applied on several dark and bright images. For performance assessment we have used Peak Signal to Noise Ratio (PSNR), absolute mean brightness error (AMBE), and Structure Similarity Index (SSIM). From experimental result, it is observed that RAMP function and hyperbolic tangent function have better image enhancement capability.

In this paper, the impact of face image pre-processing in rising the face recognition rate is presented, considering the face images with low contrast, bad or dark lighting. Three preprocessing steps including image adjustment, histogram equalization and image conversion to Joint Photographic Experts Group (JPEG) or (JPG) and Bitmap (BMP) are used to enhance the contrast and the quality of face images respectively. For dimension reduction and feature extraction purposes many techniques are adopted such as Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Kernel Principle Component Analysis KPCA and Kernel Fisher Analysis (KFA) and are used to evaluate the effect of illumination variations and image file formats on these techniques. Our results show that the proposed face databases in JPG and BMP formats produced good enhancement and increased the face recognition rate in all techniques when compared with AT&T ORL face database.

Histogram equalization is an essential technique in Image Processing. It is a contrast adjustment process using image histogram. Contrast upgrade is an imperative zone in image processing for both human and PC vision. Different Methods are proposed and some extraordinary Image Histogram strategies are Adaptive Histogram Equalization system, Recursive mean square histogram adjustment, and Global Histogram balance. In this paper, impressive histogram equalization method by changing mega pixel value is implementing. We consider some of the pixel value between ranges and then calculate its cumulative frequency. After equally distribution intensity is mapped to get a new intensity value a new histogram value.

Generally for improving contrast in digital images, HE is the method that commonly used but in result it gives unnatural artifacts like intensity saturation, over-enhancement and noise amplification. To overcome these problems there was a need to partition the image histogram, at first image histogram was partitioned into two parts and then different transformation functions were applied on each partition. After that image histogram was partitioned into many partitions and same process was applied with some additional features. DHE is the multi histogram method and CLAHE is the extension of AHE. These methods are compared to HE and found that both methods give better result than HE but DHE method also gives better result than CLAHE.

This paper presents a luminosity conserving and contrast enhancing histogram equalization method for color images. The histogram equalization is one of the ordinary methods employed for enhancing contrast in TV and images for consumer electronics where unwanted subjective deterioration are frequently occur. Although there have been many solutions to overcome the drawback of histogram equalization, however the method is for RGB color space, which is not well suited for different color spaces. To do this, we use fuzzy set to improve histogram equalization. All RGB images are firstly transformed into different color spaces, and particular channels are applied histogram equalization process. From our 20 test LC images show that HSV color space yields the favorable results in MSE by giving the luminosity conserving ability.

Mean separated histogram equalization in order to preserve the original mean brightness has been proposed. To provide the minimum mean brightness error after the histogram modification, the input image’s histogram is successively divided by the factor of 2 until the mean brightness error is satisfied the defined threshold. Then each divided group or sub-histogram will be independently equalized based on the proportional input mean. To provide the overall minimum mean brightness error, each group will be controlled by adding some certain pixels from the adjacent grey level of the next group for giving its mean near by the corresponding the divided mean. However, it still exists some little error which will be put into the next adjacent group. By successive dividing the original histogram, we found that the absolute mean brightness error is gradually decreased when the number of group is increased. Therefore, the error threshold is assigned in order to automatically dividing the original histogram for obtaining the desired absolute mean brightness error (AMBE). This process will be applied to the color image by treating each color independently.

This paper proposed brightness preserving dynamic fuzzy histogram equalization using triangular membership function which is the modified technique of histogram equalization. This modified technique, called Brightness Preserving Dynamic Fuzzy Histogram Equalization (BPDFHE), uses fuzzy statistics of digital images for their representation and processing in the fuzzy area which enables the technique to handle the approximation of gray level values in a better way for better presentation. This algorithm enhances image contrast as well as conserves the brightness very well. Some images are not available to excellent quality, so proposed Fuzzy algorithm can be used for image enhancement to improve the quality of the image.

In order to solve such problems as excessive enhancement and chessboard effect, difficult image brightness keeping and distortion in the image enhancement algorithm based on histogram equalization, an anti-distortion image contrast enhancement algorithm based on fuzzy statistics and sub-histogram equalization is proposed in this article. Specifically, the fuzzy set theory is introduced therein to convert the image into fuzzy matrix; then, by virtue of the membership function and the probability of the image gradation, the weighting function is embedded to construct the weighted fuzzy histogram calculation model; then, the mid-value of the initial image is adopted to divide the fuzzy histogram into two sub-histograms, and the corresponding cumulative density functions are defined, and the transformation models thereof are also constructed; then, the inverse transformation function is established to realize defuzzification and output the enhanced image. The experimental data show: compared with the present image enhancement algorithm based on histogram equalization, this algorithm can significantly eliminate excessive enhancement and noise amplification, thus to not only have better visual enhancement quality and anti-distortion performance, but also have maximum AIC (Average Information Contents) value and minimum NIQE (Natural Image Quality Evaluator) value.

본 연구는 ISP(Image Signal Processing) 모듈에 적용 가능한 HDR(High Dynamic Range)을 위한 Log Histogram Equalization 기법을 제안한다. 기존의 HDR 기술은 다양한 노출로 찍은 사진들을 합쳐서 한 장의 사진에서 더 넓은 동적 범위를 담아내는 방식에 집중해 왔다. 이 연구에서는 단일 노출 이미지에서도 향상된 HDR 을 구현하기 위해, 로그 함수를 이용한 히스토그램 평준화 방법을 탐구한다. 이 기법은 로그 함수의 특성을 활용하여 이미지의 대비를 증가시킨다. 또한, 룩업 테이블과 선형 근사를 도입하여 연산량을 줄이고, ISP 모듈 내에서의 실시간 처리 가능성을 높인다.

A mismatch between the training and the test conditions often causes a drastic decrease in the performance of the speech recognition systems. In this paper, non-linear transformation techniques based on histogram equalization in the acoustic feature space are studied for reducing the mismatched condition. The purpose of histogram equalization(HEQ) is to convert the probability distribution of test speech into the probability distribution of training speech. While conventional histogram equalization methods consider only the probability distribution of a test speech, for noise-corrupted test speech, its probability distribution is also distorted. The transformation function obtained by this distorted probability distribution maybe bring about miss-transformation of feature vectors, and this causes the performance of histogram equalization to decrease. Therefore, this paper proposes a new method of calculating noise-removed probability distribution by using assumption that the CDF of noisy speech feature vectors consists of component of speech feature vectors and component of noise feature vectors, and this compensated probability distribution is used in HEQ process. In the AURORA-2 framework, the proposed method reduced the error rate by over $44\%$ in clean training condition compared to the baseline system. For multi training condition, the proposed methods are also better than the baseline system.

The main purpose of image enhancement is to improve certain characteristics of an image to improve its visual quality. This paper proposes a method for image contrast enhancement that can be applied to both medical and natural images. The proposed algorithm is designed to achieve contrast enhancement while also preserving the local image details. To achieve this, the proposed method combines local image contrast preserving dynamic range compression and contrast limited adaptive histogram equalization (CLAHE). Global gain parameters for contrast enhancement are inadequate for preserving local image details. Therefore, in the proposed method, in order to preserve local image details, local contrast enhancement at any pixel position is performed based on the corresponding local gain parameter, which is calculated according to the current pixel neighborhood edge density. Different image quality measures are used for evaluating the performance of the proposed method. Experimental results show that the proposed method provides more information about the image details, which can help facilitate further image analysis.

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.

In this paper, a novel neighborhood metric of histogram equalization (HE) algorithm for contrast enhancement is presented. We present a refinement of HE using neighborhood metrics with a general framework which orders pixels based on a sequence of sorting functions which uses both global and local information to remap the image greylevels. We tested a novel sorting key with the suggestion of using the original image greylevel as the primary key and a novel neighborhood distinction metric as the secondary key, and compared HE using proposed distinction metric and other HE methods such as global histogram equalization (GHE), HE using voting metric and HE using contrast difference metric. We found that our method can preserve advantages of other metrics, while reducing drawbacks of them and avoiding undesirable over-enhancement that can occur with local histogram equalization (LHE) and other methods.

Here, we present a new framework for histogram equalization in which both local and global contrasts are enhanced using neighborhood metrics. When checking neighborhood information, filters can simultaneously improve image quality. Filters are chosen depending on image properties, such as noise removal and smoothing. Our experimental results confirmed that this does not increase the computational cost because the filtering process is done by our proposed arrangement of making the histogram while checking neighborhood metrics simultaneously. If the two methods, i.e., histogram equalization and filtering, are performed sequentially, the first method uses the original image data and next method uses the data altered by the first. With combined histogram equalization and filtering, the original data can be used for both methods. The proposed method is fully automated and any spatial neighborhood filter type and size can be used. Our experiments confirmed that the proposed method is more effective than other similar techniques reported previously.

Since the details in quasi-homogeneous region will be destroyed from the conventional global image enhancement method such as histogram equalization. This defect is caused by the saturation of gray level in equalization process. So the local histogram equalization for each quasi-homogeneous region will be used in order to improve the details in the region itself. To obtain the quasi- homogeneous regions, the original image must be segmented. Here we applied the watershed transform to the interesting image. Since the watershed transform is based on mathematical morphology, therefore, the regions touch can be effectively separated. Hence two adjacent regions which have the similar gray pixels will be split off. The process will be independently applied to three different spectral images. Then three different colors are assigned to each processed image in order to produce a color composite image. By the proposed algorithm, the result image shows the better perception on image details. Therefore, the high efficiency of image classification can be obtained by using this color image.

Histogram equalization is extensively used for image contrast enhancement in various applications due to its effectiveness and its modest functions. In image research, image enhancement is one of the most significant and arduous technique. The image enhancement aim is to improve the visual appearance of an image. Different kinds of images such as satellite images, medical images, aerial images are affected from noise and poor contrast. So it is important to remove the noise and improve the contrast of the image. Therefore, for this purpose, we apply a median filter on MR image as the median filter remove the noise and preserve the edges effectively. After applying median filter on MR image we have used intensity transformation function on the filtered image to increase the contrast of the image. Than applied the histogram equalization (HE) technique on the filtered image. The simple histogram equalization technique over enhances the brightness of the image due to which the important information can be lost. Therefore, adaptive histogram equalization (AHE) and contrast limited histogram equalization (CLAHE) techniques are used to enhance the image without losing any information.