Brightness Preserving Bi-histogram Equalization Research Articles

A comparative Study of Image Enhancement Techniques for Natural Images

Enhancement is most interesting parts in image processing field. It uses to enhance the structural appearance for picture without the degradation in the original input image. The enhancement techniques become the important key and simply extraction features by removing noise and another items inside an image. Several enhancement techniques have achieved with different and inaccurate results. The aim of this paper, the nature images quality was improved by the many enhancement techniques like of Histogram Equalization (HE), Local Histogram Equalization (LHE), Adaptive Histogram Equalization (AHE), Contrast Limited Adaptive Histogram Equalization (CLAHE), Brightness Preserving Bi-Histogram Equalization (BBHE), Dualistic Sub-Image Histogram Equalization (DSIHE) and Recursive sub-image histogram equalization (RSIHE). In evaluation step, the performance of all these techniques are examined by values measurements of SSIM (Structural Similarity Index Matrix), Entropy, Peak Signal-to-Noise Ratio (PSNR) and Signal to Noise Ratio (SNR). The comparisons of the better existing results are given because to explain the best possible technique that can be used as suitable image enhancement. The results of the enhanced of 15 nature images have showed DSIHE technique has a batter a values of SSIM and Entropy with 0.9885 and 59975 respectively. Overall, based on the PSNR and SNR values, the CLAHE technique are recorded values higher than of other six techniques in 21.2952 and 192932 values.

Mango Plant Disease Detection System Using Hybrid BBHE and CNN Approach

Detection of plant diseases plays a crucial role in taking disease control measures to increase the quality and quantity of crops produced. Plant disease automation is beneficial because it eliminates surveillance work at significant farms. As plants are a food source, diagnosing leaf conditions early and accurately is essential. This work involves a detailed learning approach that automates leaf disease detection in mango plant species. This paper presents a detection system using Brightness Preserving Bi-Histogram Equalization (BBHE) and Convolutional Neural Network (CNN). The photographs of mango leaves were first flattened, then resized and translated to their threshold value, followed by feature extraction. CNN and BBHE have extensively been used for pattern recognition. The test images of affected leaves were subsequently uploaded to the system and then matched to the ailments being trained. Training data and test data were cross-validated to balance over-adjustment and under-adjustment problems. The proposed method correctly detects the mango leaves disease at the early stage with 99.21% maximum accuracy.

Panoramic Dental Radiography Image Enhancement Using Multiscale Mathematical Morphology

Panoramic dental radiography is one of the most used images of the different dental specialties. This radiography provides information about the anatomical structures of the teeth. The correct evaluation of these radiographs is associated with a good quality of the image obtained. In this study, 598 patients were consecutively selected to undergo dental panoramic radiography at the Department of Radiology of the Faculty of Dentistry, Universidad Nacional de Asunción. Contrast enhancement techniques are used to enhance the visual quality of panoramic dental radiographs. Specifically, this article presents a new algorithm for contrast, detail and edge enhancement of panoramic dental radiographs. The proposed algorithm is called Multi-Scale Top-Hat transform powered by Geodesic Reconstruction for panoramic dental radiography enhancement (MSTHGR). This algorithm is based on multi-scale mathematical morphology techniques. The proposal extracts multiple features of brightness and darkness, through the reconstruction of the marker (obtained by the Top-Hat transformation by reconstruction) starting from the mask (obtained by the classic Top-Hat transformation). The maximum characteristics of brightness and darkness are added to the dental panoramic radiography. In this way, the contrast, details and edges of the panoramic radiographs of teeth are improved. For the tests, MSTHGR was compared with the following algorithms: Geodesic Reconstruction Multiscale Morphology Contrast Enhancement (GRMMCE), Histogram Equalization (HE), Brightness Preserving Bi-Histogram Equalization (BBHE), Dual Sub-Image Histogram Equalization (DSIHE), Minimum Mean Brightness Error Bi-Histogram Equalization (MMBEBHE), Quadri-Histogram Equalization with Limited Contrast (QHELC), Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Gamma Correction (GC). Experimentally, the numerical results show that the MSTHGR obtained the best results with respect to the Contrast Improvement Ratio (CIR), Entropy (E) and Spatial Frequency (SF) metrics. This indicates that the algorithm performs better local enhancements on panoramic radiographs, improving their details and edges.

Design and Implementation of Brightness Configurable Bi-Histogram Equalization Based on FPGA

In order to improve the contrast of images in different brightness environments, based on brightness preserving Bi-Histogram Equalization(BBHE), a Brightness configurable Bi-Histogram Equalization(BCBHE) system is proposed and implemented by FPGA in this paper. The system implements the BBHE algorithm, and adds the brightness configurable logic unit to realize the brightness configurable image contrast enhancement. The test results of the video processing platform of Genesys 2 development board show that: the design obtains BBHE images with different brightness configurations, and achieves the best contrast enhancement visual effect by setting image brightness threshold in different field environment. At the same time, based on the parallel operation characteristics of the FPGA, it meets the requirements of real-time image processing.

A fully customized enhancement scheme for controlling brightness error and contrast in magnetic resonance images

Abstract Enhancement schemes are said to be ideal when they produce images with maximum contrast and minimum brightness error. But these are two conflicting objectives. Their relative significance is always application dependent. No mechanism is available in the conventional histogram equalization to control contrast and brightness error. Even though methods like Contrast Limited Adaptive Histogram Equalization (CLAHE) and Weighted Threshold Histogram Equalization (WTHE) have such provisions, tuning their multiple operational parameters simultaneously is laborious. A global contrast mapping scheme for Magnetic Resonance (MR) images which equip the user to customize its performance based on the application is proposed in this paper. In this scheme, the histogram is clipped with respect to a threshold between the mean and maximum probability density values in it, determined by the arbitrary clip-limit, ‘α’, 0 ≤ α ≤ 1. The clipped or residue pixels are reallocated to the histogram bins according to the relative vacancy in them. Prior to the equalization, the Cumulative Probability Density (CPD) computed from the re-normalized histogram is expanded to increase the dynamic range of the enhanced image. In terms of Relative Enhancement in Contrast (REC), Absolute Mean Brightness Error (AMBE), Structural Similarity Index Metric (SSIM) and Saturation Evaluation Index (SEI), the proposed method is observed to be superior to Brightness Preserving Bi-histogram Equalization, WTHE and CLAHE on 30 sets of MR images.

Combination of Brightness Preserving Bi-Histogram Equalization and Discrete Wavelet Transform using LUV Color Space for Image Enhancement

Combination of Brightness Preserving Bi-Histogram Equalization and Discrete Wavelet Transform using LUV Color Space for Image Enhancement

Brain early infarct detection using gamma correction extreme-level eliminating with weighting distribution.

According to the statistic from World Health Organization (WHO), stroke is one of the major causes of death globally. Computed tomography (CT) scan is one of the main medical diagnosis system used for diagnosis of ischemic stroke. CT scan provides brain images in Digital Imaging and Communication in Medicine (DICOM) format. The presentation of CT brain images is mainly relied on the window setting (window center and window width), which converts an image from DICOM format into normal grayscale format. Nevertheless, the ordinary window parameter could not deliver a proper contrast on CT brain images for ischemic stroke detection. In this paper, a new proposed method namely gamma correction extreme-level eliminating with weighting distribution (GCELEWD) is implemented to improve the contrast on CT brain images. GCELEWD is capable of highlighting the hypodense region for diagnosis of ischemic stroke. The performance of this new proposed technique, GCELEWD, is compared with four of the existing contrast enhancement technique such as brightness preserving bi-histogram equalization (BBHE), dualistic sub-image histogram equalization (DSIHE), extreme-level eliminating histogram equalization (ELEHE), and adaptive gamma correction with weighting distribution (AGCWD). GCELEWD shows better visualization for ischemic stroke detection and higher values with image quality assessment (IQA) module. SCANNING 38:842-856, 2016. © 2016 Wiley Periodicals, Inc.

Performance Evaluation of Spatial Domain Contrast Enhancement Techniques for Underwater Images

Image enhancement methods are widely used for improving the feature and quality of the images. Researchers have designed various contrast enhancement methods in the past. But since underwater image contain low contrast and resolution due to poor visibility conditions and refractions of light rays. Thus analysis and identification of the underwater object is a challenging and difficult task. In this paper presents a comparative analysis of various contrast enhancement techniques for such underwater images. Performance of contrast limited adaptive histogram equalization (CLAHE) method is compared with contrast stretching, global histogram equalization (HE) Brightness preserving Bi-histogram equalization (BBHE) and finally Dual Sub-Image histogram equalization (DSIHE) methods. Different colour underwater images are enhanced in the RGB colour space. The performance is evaluated based on discrete entropy, signal to noise ratio (SNR) and Standard deviation difference and the mean brightness. Performance of methods is evaluated under various type of underwater image environment. General Terms Image processing, Image enhancement, Underwater Images

A Comparative Study of Histogram Equalization Based Image Enhancement Techniques for Brightness Preservation and Contrast Enhancement

Histogram Equalization is a contrast enhancement technique in the image processing which uses the histogram of image. However histogram equalization is not the best method for contrast enhancement because the mean brightness of the output image is significantly different from the input image. There are several extensions of histogram equalization has been proposed to overcome the brightness preservation challenge. Contrast enhancement using brightness preserving bi-histogram equalization (BBHE) and Dualistic sub image histogram equalization (DSIHE) which divides the image histogram into two parts based on the input mean and median respectively then equalizes each sub histogram independently. This paper provides review of different popular histogram equalization techniques and experimental study based on the absolute mean brightness error (AMBE), peak signal to noise ratio (PSNR), Structure similarity index (SSI) and Entropy.

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.

Improved brightness preserving bi-histogram equalization algorithm

Based on Brightness preserving Bi-Histogram Equalization (BBHE),an improved algorithm of gray image enhancement was proposed.An appropriate threshold,which was selected based on the entropy of the output image and the difference between the mean brightness of input and output images,was selected to cut the image,then BBHE and gray lever homogenization were performed respectively.This method makes the brightness mean error as small as possible and the entropy of output image as large as possible.Meanwhile,it can prevent over-enhancement.The experimental results prove that the new method has better performance on image enhancement.

Comparison of Image Enhancement Methods for the Effective Diagnosis in Successive Whole-Body Bone Scans

Whole-body bone scan is one of the most frequent diagnostic procedures in nuclear medicine. Especially, it plays a significant role in important procedures such as the diagnosis of osseous metastasis and evaluation of osseous tumor response to chemotherapy and radiation therapy. It can also be used to monitor the possibility of any recurrence of the tumor. However, it is a very time-consuming effort for radiologists to quantify subtle interval changes between successive whole-body bone scans because of many variations such as intensity, geometry, and morphology. In this paper, we present the most effective method of image enhancement based on histograms, which may assist radiologists in interpreting successive whole-body bone scans effectively. Forty-eight successive whole-body bone scans from 10 patients were obtained and evaluated using six methods of image enhancement based on histograms: histogram equalization, brightness-preserving bi-histogram equalization, contrast-limited adaptive histogram equalization, end-in search, histogram matching, and exact histogram matching (EHM). Comparison of the results of the different methods was made using three similarity measures peak signal-to-noise ratio, histogram intersection, and structural similarity. Image enhancement of successive bone scans using EHM showed the best results out of the six methods measured for all similarity measures. EHM is the best method of image enhancement based on histograms for diagnosing successive whole-body bone scans. The method for successive whole-body bone scans has the potential to greatly assist radiologists quantify interval changes more accurately and quickly by compensating for the variable nature of intensity information. Consequently, it can improve radiologists' diagnostic accuracy as well as reduce reading time for detecting interval changes.

Brightness preserving histogram equalization with maximum entropy: a variational perspective

Histogram equalization (HE) is a simple and effective image enhancing technique, however, it tends to change the mean brightness of the image to the middle level of the permitted range, and hence is not very suitable for consumer electronic products, where preserving the original brightness is essential to avoid annoying artifacts. This paper proposes a novel extension of histogram equalization, actually histogram specification, to overcome such drawback as HE. To maximize the entropy is the essential idea of HE to make the histogram as flat as possible. Following that, the essence of the proposed algorithm, named brightness preserving histogram equalization with maximum entropy (BPHEME), tries to find, by the variational approach, the target histogram that maximizes the entropy, under the constraints that the mean brightness is fixed, then transforms the original histogram to that target one using histogram specification. Comparing to the existing methods including HE, brightness preserving bi-histogram equalization (BBHE), equal area dualistic sub-image histogram equalization (DSIHE), and minimum mean brightness error bi-histogram equalization (MMBEBHE), experimental results show that BPHEME can not only enhance the image effectively, but also preserve the original brightness quite well, so that it is possible to be utilized in consumer electronic products.

저대비 영상을 위한 영상향상 기법들의 비교연구

The principal objective of enhancement methods is to process an image so that the output image is more suitable than the original image lot a specific application. Images taken in the night can be low-contrast images because of poor environments. In this paper, we compared the performance of Image Contrast Enhancement Technique Using Clustering Algorithm(ICECA) with those of color adjustment methods such as Histogram Equalization(HE), Brightness Preserving Bi-Histogram Equalization(BBHE), and the Multi-Scale Refiner(MSR). We compared these methods by applying the image enhancement methods to a set of diverse images.

SU‐FF‐I‐38: Mask‐Based vs. Reverse Engineering‐Based Strategies for Skin‐Line Enhancement in Low Contrast Projection Mammograms

Purpose: This paper presents two new strategies for skin‐line enhancement in mammograms based on brightness preserving bi‐histogram equalization (BBHE) [1] framework during window and level process. Method and Materials: Image enhancement is performed using two different strategies based on BBHE framework: (A) masked‐based strategy, and (B) reverse engineering approach. In the first approach, the image is divided into two regions: skin‐line zone mask and the rest of the breast region. The skin‐line zone mask is computed by subtracting the binary images obtained using two different threshold techniques: whole breast region threshold using Ojala's method [2] and stroma breast region threshold using Otsu's method [3]. The BBHE is then applied in this masked zone and the remaining breast region, thereby boosting the skin‐line zone of the mammograms. The second approach uses the reverse engineering technique where the enhancement is started in the reverse direction from skin‐line towards the parenchyma. The bands are taken from the outer region of the breast moving inward. This method is implemented by performing BBHE on a significant band (say 50 pixels wide) on the image and replacing only a small band (say 10 pixels wide) by the enhanced BBHE band. This is repeated until the whole image was enhanced. This method performs very well on mammograms with very low contrast in the skin‐line zone. Results: Both techniques demonstrated a considerable boosting in the skin‐line zone, which was the goal of the enhancement process. The lesion conspicuity is improved and skin‐line edges are well seen during window level. The algorithm has been tested on 15 MIAS mammograms. Conclusion: Both implemented methods show enhancement in the skin‐line region. We are currently testing its robustness on other mammogram databases.

Preserving brightness in histogram equalization based contrast enhancement techniques

Histogram equalization (HE) has been a simple yet effective image enhancement technique. However, it tends to change the brightness of an image significantly, causing annoying artifacts and unnatural contrast enhancement. Brightness preserving bi-histogram equalization (BBHE) and dualistic sub-image histogram equalization (DSIHE) have been proposed to overcome these problems but they may still fail under certain conditions. This paper proposes a novel extension of BBHE referred to as minimum mean brightness error bi-histogram equalization (MMBEBHE). MMBEBHE has the feature of minimizing the difference between input and output image's mean. Simulation results showed that MMBEBHE can preserve brightness better than BBHE and DSIHE. Furthermore, this paper also formulated an efficient, integer-based implementation of MMBEBHE. Nevertheless, MMBEBHE also has its limitation. Hence, this paper further proposes a generalization of BBHE referred to as recursive mean-separate histogram equalization (RMSHE). RMSHE is featured with scalable brightness preservation. Simulation results showed that RMSHE is the best compared to HE, BBHE, DSIHE, and MMBEBHE.