Image Enhancement using Various Histogram Equalization Techniques

Color image enhancement refers to improving the visual quality of color images by adjusting various attributes such as brightness, contrast, sharpness, and color balance. This enhancement aims to make the images more visually appealing, precise, and easier to interpret, often by accentuating essential features or details while minimizing noise or distortion. Techniques vary from simple adjustments of brightness and contrast to sophisticated algorithms. Effective image enhancement is crucial in numerous fields like medical imaging; enhancing the medical image is vital for accurate diagnosis and treatment planning, as well as remote sensing for improved satellite and aerial imagery. In security and surveillance, enhanced clarity in footage from surveillance cameras, especially in low-light scenarios, is crucial for identifying subjects and activities. However, image enhancement faces several challenges, like noise amplification and Over-enhancement, leading to unnatural-looking images with exaggerated or distorted features. In this paper, low-exposed or night color images are considered for enhancement, and this paper introduces exposure-based recursive histogram equalization techniques along with an Energy Curve instead of the conventional histogram; the energy curve is similar to a histogram based on spatial Contextual Information of an image. The proposed methods are Recursive Exposure-based Sub-image Histogram Equalization, Recursively Separated Exposure-based Sub-image Histogram Equalization, and Exposure-based Sub-image Histogram Equalization techniques considering spatial contextual information of images using an Energy Curve to improve results. These methods were tested on several publicly available datasetswith low-contrast color images and compared with HE, BBHE, DSHE, CLAHE, ESIHE, R-ESIHE, and RS-ESIHE. The proposed techniques are evaluated using parameterslikeAMBE, PSNR, MSE, Entropy, SSIM, and FSIM. The average PSNR values for eight images (taken from TeV DICeneter https://tev.fbk.eu/resources/imageenhancement) using the techniques mentioned above were 9.10817, 20.80568, 12.82645, 10.39347, 16.27458, 15.19979, and 14.59595, respectively. In contrast, the proposed ESIHE with Energy Curve achieved a PSNR of 22.31585, outperforming other methods across multiple metrics, particularly excelling in noise reduction (PSNR), error minimization (MSE: 193.44), structural similarity (SSIM: 0.733319), and feature retention (FSIM: 0.917678). The comparison demonstrates that enhancement methods can be significantly improved by considering spatial contextual information.

This paper demonstrates the significance of histogram processing of an image particu larly the histogram equalization (HE). It is one of the widely used image enhancement technique. It has become a popular technique for contrast enhancement because the method is simple and effect ive. The basic idea of HE is to re-map the gray levels of an image. Here we propose two different techniques of Histogram Equalizat ion namely, the global HE and local HE. The Histogram Equalization has been performed in the MATLA B environ ment. The merits and demerits of both techniques of Histogram Equalization have also been discussed. It is seen after exhaustive experimentation on a nu mber of sample images that the proposed image enhancement techniques can be considered as an imp rovement over the inbuilt MATLAB function histeq.

This research aims to evaluate the effect of com- binning histogram equalization techniques, such as adaptive equalization, BI-Histogram Equalization, and limited contrast adaptive histogram equalization, with the Butterworth filter to enhance the quality of brain images, especially in terms of image sharpness and noise removal. Histogram equalization techniques are applied to improve the illumination distribution and contrast of medical images, while the Butterworth filter is used to reduce noise and enhance sharpness in brain images. The results are analyzed using performance measurement metrics: image contrast, gradient entropy, wavelet sum, and histogram entropy. The different effects of each technique on the processed brain images are compared. The results show that the proposed method of combining histogram equalization techniques with the Butterworth filter produces high-quality photos. The numerical results of comparing traditional histogram equalization techniques and the proposed method demonstrate this. This technique offers a valuable framework for enhancing brain images, aiding accurate medical diagnosis.

This paper presents a comparative analysis of fundus image enhancement techniques to detect diabetic retinopathy (DR). Medical images frequently suffers from non-uniform illumination, poor contrast and noise, thus these images have to go through pre-processing stage. For image enhancement many techniques are proposed based on spatial domain (histogram). However, these methods usually decline to produce suitable consequences for non-uniform illumination and wide-ranging of low-contract. The comparative analysis and performance evaluation of various enhancement techniques will help in choosing most suitable technique which may significantly improve the detection of diabetic retinopathy. In this paper histogram equalization (HE), adaptive histogram equalization (ADHE), contrast limited adaptive histogram equalization (CLAHE) and exposure based sub-image histogram equalization (ESIHE) techniques are compared for pre-processing of fundus image for detecting DR. For fair analysis of these techniques, histogram, SNR, entropy, absolute mean brightness error and peak signal-to-noise ratio (PSNR) of fundus images are analyzed by using MATLAB.

In this work, success of image feature extraction based on morphological is examined by various histogram equalization techniques. In comparison process, traditional histogram equalization, adaptive histogram equalization and local histogram equalization methods are used. In addition to that, effects on different structure element parameters is examined success of image feature extraction process.

This paper proposes a image enhancement and segmentation method for mammographic images, which is based on the pixel intensity transformation, spatial filtering, edge detection, and region growing techniques. This method consists of two approaches. The first approach is applying intensity transformation, logarithmic transformation, contrast stretching, histogram equalization and spatial filtering techniques separately to enhance the mammogram images. In the second approach is applying edge detection operators, local and global thresholding techniques used to extract the suspicious regions from back ground tissue.

The popular histogram equalisation (HE) technique, which was developed to improve the image contrast, sometimes may also be misused to hide intensity variations in tampered images with ill intention. The authors investigate how existing image forensic techniques may fail to detect HE operation in highly compressed and low-resolution images. They then propose an algorithm to detect whether a given image (either uncompressed or JPEG compressed) has undergone the HE process or not. It is based on the frequency domain analysis of image histogram and exploits the difference in DC and AC coefficients in histogram's discrete Fourier transform. It can detect HE operation even if the image is saved in JPEG format after the equalisation, where most the existing algorithms fail. The extensive computer simulations over large dataset show the effectiveness of the proposed algorithm.

Digital image processing is versatile research in this era. Many researchers implement different types of organizations like image restoration, image enhancement, color image processing, image segmentation etc. Image enhancement technique is among the simplest and most appealing area of digital image processing. Enhancement techniques like brightness preservation, contrast enhancement highlight certain features means depend which part of the image want to be enhance some application some input image including noise, reduction or removal of noise is also form of image enhancement. Brightness preservation has enhanced visual quality of digital image so that the limitation contained in these images is used for various applications in a better way. A very popular technique for image enhancement is histogram equalization (HE) and curvelet transformation. HE technique is commonly employed for image enhancement because of its simplicity and comparatively better performance on almost all types of images. Another widely used technique is curvelet transformation. This technique is identified and separate bright regions of image but more error rate and low peak signal to noise ratio(PSNR), result of this technique is brightness preservation level is low and output image is gray. This paper design a hybrid model through discrete cosine transformation, discrete wavelet transformation and combine output of both techniques with image fusion. Proposed algorithm enhanced features and removal noise by decomposition of image using DWT and discrete cosine transformation, adaptive histogram equalization is very important part in this algorithm for smooth image. The tested results of different images are comparing with previous method, generating result with different parameters; less mean square error and high PSNR for improve the quality of an image. This paper presents a hybrid model used various parameter for enhance images like satellite images, medical images etc.

This research has an objective to implement histogram equalization technique in the evaluation of characteristics medical images, especially for statistical characterization of medical images. Implementation starting with the pre-processing of the image by converting RGB medical images into grayscale images, followed by determination of the Region of Interest (ROI) and cropping methods. The next process was to increase the quality of medical images by implementing histogram equalization technique. The medical image characteristic was evaluated using characteristic statistical methods with their parameters are mean value, standard deviation, entropy, skewness, and kurtosis. The Medical image was used in this research is a medical image of advanced breast cancer. This research compared the characteristics statistical characterization of medical images obtained with the original medical image which has enhanced use histogram equalization technique. The results of implementation histogram equalization obtain medical image feature extraction results for the value of mean, entropy, skewness, and kurtosis become smaller and standard deviation value was increase.

Histogram equalization is an important image enhancement technique widely used in image processing. It is good method of image enhancement indeed; but it doesn't consider about human visual response to light. We know that human visual response to light is non-linear, which is somewhat likes natural logarithmic function. So the gray levels of histogram equalized, image is not really equally distributed for human eyes. In fact, the ordinary histogram equalization is not visually equalized. According to the human visual model, this article puts forward a new algorithm or image histogram modification, with which modified image has visually equal gray level distribution. This algorithm is conducted while assuming exiting a visual image whose histogram can be counted and is equal distributed. Experiment proves that this algorithm is better than ordinary image histogram equalization technique in image enhancement. It fits human visual response well. It can be widely used in digital image processing.

The problem of improving the efficiency of contrast enhancement for complex low-contrast images in an automatic mode with an acceptable level of computational costs is considered. A new approach to image enhancement by parameter-free intensity transformation based on an estimate of the bivariate distribution of brightness is proposed. Analysis of the bivariate distribution of brightness enables to significantly improve the efficiency of image contrast enhancement. Based on this approach, a new generalized description for the procedure of intensity transformation based on the analysis of the marginal cumulative distribution function of image brightness is proposed. The relationship between the proposed generalized description of the intensity transformation and the traditional definition of histogram equalization is shown. Based on proposed description, a new technique of modified histogram equalization is proposed. The research confirms the effectiveness of the proposed technique. The proposed technique of modified histogram equalization provides an increase in the efficiency of contrast enhancement compared to the well-known traditional technique of histogram equalization.

Logarithmic transformation and histogram equalization (HE) are well-known image enhancement techniques in spatial domain. These techniques are very popular for contrast enhancement because the methods are simple and effective. The basic idea of HE is to remap the gray levels of an image. However, HE tends to introduce some annoying artifacts and unnatural enhancement. Here, we propose an integration of the two techniques at pixel level, doing first the HE and then using logarithmic transformation for mapping purpose. Both the global HE and local HE are preformed on the input image pixels. The histogram equalization has been performed in the MATLAB environment. We have experimented the proposed techniques over a number of sample images and found to produce much better results compared to image enhancement using the inbuilt MATLAB function histeq.

Dental, oral and maxillofacial region are very sensitive so low radiation imaging technique like Cone Beam Computed Tomography (CBCT) is useful for investigation of diseases. But due to low radiation, quality of images is not fine they have noise and low contrast in visual impression. So for dentistry diagnostic purpose it is important to pre-process the CBCT image. This paper focuses on improvement of contrast, brightness and noise reduction of dental CBCT images by using Bi Histogram Equalization technique with bent activation function and adaptive median filter for noise reduction. The output image has been analyzed and evaluated using peak value ratio in term of Signal versus Noise (PSNR) and matrix of Structure in term of Similarity Index (SSIM). From the experimental results, it has been found that the adaptive median and Bi histogram with bent activation function has a better noise and enhancement capability as compare to convectional histogram method.

This paper presents a new method of histogram equalization for grayscale images, which is called the gradient based histogram equalization. The histogram equalization is performed on the image filtered by means of gradient operators. The proposed method is simple, fast, and the preliminary experimental examples with different images show that the method is effective for image enhancement. While preserving the range and mean intensity of the image, the new method allows for reducing the standard deviation and significantly straitening the graph of the histogram, when comparing with the traditional (or global) histogram equalization.

There is a recent trend change in using non-Bayer Color Filter Array (CFA) patterns, allowing superior image quality in low-light conditions. However, these new CFA patterns have weak point on full image reconstruction showing severe visual artifacts and low details reconstruction after demosaicing. In this work, we address aforementioned problems by using deep learning approach for new CFA type - Nonacell or Nonapixel, introduced by Samsung 108MP HMX CMOS image sensor. Experimental results show that proposed method not only allows suppression of visual artifacts and perfect details and edge restoration, but also shows superior objective image quality, exceeding 40dB in CPSNR for popular Kodak dataset. Finally, our method is computationally efficient due to network structure and feasible for on-device deployment after optimization.