Histogram Equalization-A Simple but Efficient Technique for Image Enhancement

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.

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.

Magnetic Resonance Imaging (MRI) is a medical imaging technique used for analyzing and diagnosing diseases such as cancer or tumor in a brain. In order to analyze these diseases, physicians require good contrast scanned images obtained from MRI for better treatment purpose as it contains maximum information of the disease. MRI images are low-contrast images which lead to difficulty in diagnoses, hence better localization of image pixels is required. Histogram equalization techniques help to enhance the image so that it gives an improved visual quality and a well-defined problem. The contrast and brightness are enhanced in such a way that it does not lose its original information and the brightness is preserved. In this paper, we compared the different equalization techniques which are critically studied and elaborated. Various parameters are calculated and tabulated, finally concluded the best among them.

Image Enhancement is a method that converts a poor quality image into a image of superior quality. This paper basically intends to enhance the quality of a image using the Histogram Equalization technique on MATLAB (R2014a) software and the outcome of the image parameters before and after applying Histogram Equalization are discussed. The statistical parameters of the image histogram such as mean and median can be used analyse and show an image of improvised image quality. The method is used to improve the image quality of an X-Ray image of a human bone. This paper also discusses the modern and enhanced techniques of Histogram Equalization and their merits over the traditional Histogram Equalization technique.

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.

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.

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.

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.

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.

Histogram Equalization (HE) is a simple, effective and widely used contrast enhancement technique as it can automatically define the intensity transformation function based on statistical characteristics of the image, but it tends to change the mean brightness of the image to the middle level of the gray level range. HE also produces saturation effects by extremely pushing the intensities towards the right or the left side of the histogram. To surmount these drawbacks, Clipping or Plateau Histogram Equalization techniques for brightness preserving and contrast enhancement have been proposed, but, these are not suitable for automatic systems because of manual selection of threshold level. Self-Adaptive Plateau Histogram Equalization (SAPHE) selects the threshold level automatically, but the process is relatively complicated and sometimes fails in execution. To overcome these drawbacks, a Modified Self - Adaptive Plateau Histogram Equalization with Mean threshold (Modified SAPHE-M) is proposed in this paper and compared the experimental results with Histogram Equalization (HE), Self-Adaptive Plateau Histogram Equalization (SAPHE) and Modified Self-Adaptive Plateau Histogram Equalization (Modified SAPHE) by using image quality measures such as Absolute Mean Brightness Error (AMBE) and Peak-Signal to Noise Ratio (PSNR).

This paper presents a generalized image contrast enhancement technique which equalizes perceived brightness based on the Heinemann contrast discrimination model. This is a modified algorithm which presents an improvement over the previous study by Mokrane in its mathematically proven existence of a unique solution and in its easily tunable parameterization. The model uses a log-log representation of contrast luminosity between targets and the surround in a fixed luminosity background setting. The algorithm consists of two nonlinear gray-scale mapping functions which have seven parameters, two of which are adjustable Heinemann constants. Another parameter is the background gray level. The remaining four parameters are nonlinear functions of gray scale distribution of the image, and can be uniquely determined once the previous three are given. Tests have been carried out to examine the effectiveness of the algorithm for increasing the overall contrast of images. It can be demonstrated that the generalized algorithm provides better contrast enhancement than histogram equalization. In fact, the histogram equalization technique is a special case of the proposed mapping.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

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.

Contrast Enhancement is a technique which comes into the part of Image Enhancement. Contrast Enhancement is used to enhance the visual quality of any captured or other image. Contrast Enhancement can be performed with the help of Histogram equalization (HE). In this technique, the image is collected in the gray scale allocation. The image is then partitioning and applying adaptive Histogram equalization (AHE). Fuzzy logic provides a set of logics which enhance the contrast and visibility of any image. In this technique, the visual quality and the contrast of image will change and then compare these results with previous techniques. The performance of several established image enhancement techniques is presented in terms of different parameters like Absolute mean brightness error (AMBE), Peak signal to noise ratio (PSNR), contrast and Visual quality.

Accurate semantic segmentation of 3D point clouds is a long-standing problem in remote sensing and computer vision. Due to the unstructured nature of point clouds, designing deep neural architectures for point cloud semantic segmentation is often not straightforward. In this work, we circumvent this problem by devising a technique to exploit structured neural architectures for unstructured data. In particular, we employ the popular convolutional neural network (CNN) architectures to perform semantic segmentation of LiDAR data. We propose a projection-based scheme that performs an angle-wise slicing of large 3D point clouds and transforms those slices into 2D grids. Accounting for intensity and reflectivity of the LiDAR input, the 2D grid allows us to construct a pseudo image for the point cloud slice. We enhance this image with low-level image processing techniques of normalization, histogram equalization, and decorrelation stretch to suit our ultimate object of semantic segmentation. A large number of images thus generated are used to induce an encoder-decoder CNN model that learns to compute a segmented 2D projection of the scene, which we finally back project to the 3D point cloud. In addition to a novel method, this article also makes a second major contribution of introducing the enhanced version of our large-scale public PC-Urban outdoor dataset which is captured in a civic setup with an Ouster LiDAR sensor. The updated dataset (PC-Urban_V2) provides nearly 8 billion points including over 100 million points labeled for 25 classes of interest. We provide a thorough evaluation of our technique on PC-Urban_V2 and three other public datasets.

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.