Dynamic Histogram Equalization Research Articles

Precision Identification of Locally Advanced Rectal Cancer in Denoised CT Scans Using EfficientNet and Voting System Algorithms.

Local advanced rectal cancer (LARC) poses significant treatment challenges due to its location and high recurrence rates. Accurate early detection is vital for treatment planning. With magnetic resonance imaging (MRI) being resource-intensive, this study explores using artificial intelligence (AI) to interpret computed tomography (CT) scans as an alternative, providing a quicker, more accessible diagnostic tool for LARC. In this retrospective study, CT images of 1070 T3-4 rectal cancer patients from 2010 to 2022 were analyzed. AI models, trained on 739 cases, were validated using two test sets of 134 and 197 cases. By utilizing techniques such as nonlocal mean filtering, dynamic histogram equalization, and the EfficientNetB0 algorithm, we identified images featuring characteristics of a positive circumferential resection margin (CRM) for the diagnosis of locally advanced rectal cancer (LARC). Importantly, this study employs an innovative approach by using both hard and soft voting systems in the second stage to ascertain the LARC status of cases, thus emphasizing the novelty of the soft voting system for improved case identification accuracy. The local recurrence rates and overall survival of the cases predicted by our model were assessed to underscore its clinical value. The AI model exhibited high accuracy in identifying CRM-positive images, achieving an area under the curve (AUC) of 0.89 in the first test set and 0.86 in the second. In a patient-based analysis, the model reached AUCs of 0.84 and 0.79 using a hard voting system. Employing a soft voting system, the model attained AUCs of 0.93 and 0.88, respectively. Notably, AI-identified LARC cases exhibited a significantly higher five-year local recurrence rate and displayed a trend towards increased mortality across various thresholds. Furthermore, the model's capability to predict adverse clinical outcomes was superior to those of traditional assessments. AI can precisely identify CRM-positive LARC cases from CT images, signaling an increased local recurrence and mortality rate. Our study presents a swifter and more reliable method for detecting LARC compared to traditional CT or MRI techniques.

A hybrid thyroid tumor type classification system using feature fusion, multilayer perceptron and bonobo optimization.

Thyroid tumor is considered to be a very rare form of cancer. But recent researches and surveys highlight the fact that it is becoming prevalent these days because of various factors. This paper proposes a novel hybrid classification system that is able to identify and classify the above said four different types of thyroid tumors using high end artificial intelligence techniques. The input data set is obtained from Digital Database of Thyroid Ultrasound Images through Kaggle repository and augmented for achieving a better classification performance using data warping mechanisms like flipping, rotation, cropping, scaling, and shifting. The input data after augmentation goes through preprocessing with the help of bilateral filter and is contrast enhanced using dynamic histogram equalization. The ultrasound images are then segmented using SegNet algorithm of convolutional neural network. The features needed for thyroid tumor classification are obtained from two different algorithms called CapsuleNet and EfficientNetB2 and both the features are fused together. This process of feature fusion is carried out to heighten the accuracy of classification. A Multilayer Perceptron Classifier is used for classification and Bonobo optimizer is employed for optimizing the results produced. The classification performance of the proposed model is weighted using metrics like accuracy, sensitivity, specificity, F1-score, and Matthew's correlation coefficient. It can be observed from the results that the proposed multilayer perceptron based thyroid tumor type classification system works in an efficient manner than the existing classifiers like CANFES, Spatial Fuzzy C means, Deep Belief Networks, Thynet and Generative adversarial network and Long Short-Term memory.

Automated weather forecasting and field monitoring using GRU-CNN model along with IoT to support precision agriculture

India's agricultural sector is dwindling, which has an impact on ecosystem output. Smart farming increases crop productivity by reducing waste and making effective use of fertiliser. Various Machine Learning techniques together with IoT enabled are developed for precision agriculture. However, the existing techniques face difficulties in forecasting weather and predicting disease accurately. In this work, a system is developed to automate weather forecasting and field monitoring using sensors. Wireless transmission is used to transfer sensor data to a web server database. In the first step, the collected data is pre-processed using normalization and mean based missing value imputation to convert the raw data into meaningful data. Pre-processed data is then fed into Gated Recurrent Unit (GRU) for forecasting the weather condition. In the second step, crop images acquired from sensors for disease prediction are pre-processed using Adaptive Gaussian Filter for noise removal and Dynamic Histogram Equalization for contrast enhancement of image. Pre-processed images are then fed into ResNet50 for feature extraction and classification. Using these predicted data, in case of any horrible weather conditions and soil conditions, remedial action will be automatically taken by the systems. On the other hand, regarding the information about horrible conditions and in presence of pests and diseases, an alert message will be sent to the farmers. The proposed method is tested with several metrics which attain better performance like 94% accuracy for weather forecasting and 98% accuracy for field monitoring. Thus the proposed IoT and deep learning based model can support farmers to achieve a high quantity of crop production in lesser time.

Pneumonia screening on chest X-rays with optimized ensemble model

Pneumonia is a lung illness that may result from a variety of various viral diseases and may be lethal. It might be difficult to diagnose and treat pneumonia on chest X-ray pictures because pneumonia and other lung illnesses are closely connected. As a result, the present methods for forecasting pneumonia cannot achieve meaningful levels of accuracy. This study aids in identifying diseases from chest X-ray images that don't exhibit the same symptoms as pneumonia. Five stages will be included in the projected model: (a) pre-processing, (b) segmentation, (c) feature extraction, (d) optimal feature selection, and (e) detection. The gathered image is initially pre-processed using Dynamic Histogram Equalization (DHE), followed in this case by median filtering. At the segmentation phase, by utilizing the Enhanced Watershed Segmentation, the ROI is separated from the background region of the pre-processed image. Additionally, in the feature extraction step, features based on Local Gradient Increasing Pattern (LGIP), Harmonic Local Gradient Pattern (hLGP), Improved Local Ternary Pattern (ILTP), and Improved Local Gradient Pattern (ILGP) will be derived from the isolated ROI areas. Using the proposed AHGOA (Archimedes-assisted Henry Gas Optimization Algorithm) model, the best characteristics from these retrieved features were selected. This is the best option for avoiding the dimensionality curse. The selected EC + AHGOA method obtain good accuracy (∼0.95) for tuning percentage 70 in pneumonia diagnosis from Chest X-ray images than some other previous techniques includes EC + AOA (∼0.92), EC + HGSO (∼0.93), EC + HGS (∼0.88), EC + PRO (∼0.90), and EC + BES (∼0.89).

Performance Analysis of Graph theory-based Contrast Limited Adaptive Histogram Equalization for Image Enhancement

Nowadays, image enhancement has become a major area of research because of the development of applications that are based on vision.Several digital image processing systems employ such image enhancement strategies with the help of graph theory. As the visibility level in low contrast image features is very less,several image enhancement strategies have been introduced with spatial transformations to enhance image qualityfor improved visualization. Nowadays, image processing plays an important role in the analysis of a patient’s health status and has become extremely popular in medical areas for a wide range of clinical assessments. Generally, medical images contain several complex areas and thereby,few pre-processing approaches are applied to reduce the challenges that occur during different phases of the CAD system. Furthermore, because of external noise interferences, poor illuminating settings as well as other imaging device limitations, the clinical diagnosis becomes a challenging process and medical images do not provide important information for precise categorization. Medical images are available in a variety of applications such as computed tomography, Magnetic Resonance Imaging (MRI), mammography, chest X-ray (CXR), and many more. Only the pixel intensity variations between different areas as well as object boundary information are essential for categorization and must be enhanced simultaneously. As a result, the rate of classification in medical images and intensity are increased so that every object during the analysis can be easily identified. The main goal of any image enhancement process is to enhance the quality of the image by reducing noise and on other hand by using three different algorithms such as Luminance Modulation (LM), Gradient Modulation (GM), and Dynamic Histogram Equalization (DHE). These three algorithms are designed with the help of graph theory for effective preservation of edges, losses, and efficient smoothing and to preserve the basic information without any modifications. Image restoration is also referred to as image enhancement and it is concerned with the precise assessment of real images. Generally, the degradation process is not included in many of the image-enhancement approaches that are already existing. Furthermore, with the application of enhancement techniques, the degradation process for medical images results in some significant performance loss. Several techniques have been proposed and the technique which is examined in this research is image enhancement that is based on histogram which mainly concentrates on equalizing the histogram of values. Histogram Equalization (HE) possesses a few basic properties such as altering spatial patterns as well as intensity which in turn results in significant challenges in medical imaging. As a result,Contrast Limited Adaptive Histogram Equalization (CLAHE) is proposed in this work as a feasible approach for medical image analysis to address the problem. The suggested research work demonstrates that the intensity limiting image enhancement with histogram equalization detects the irregularities in dense mammograms with enhanced quality.

An Efficient USE-Net Deep Learning Model for Cancer Detection

Breast cancer (BrCa) is the most common disease in women worldwide. Classifying the BrCa image is extremely important for finding BrCa at an earlier stage and monitoring BrCa during treatment. The computer-aided detection methods have been used to interpret BrCa and improve the detection of BrCa during the screening and treatment stages. However, if a new BrCa image is generated for the treatment, it will not classify correctly. The main objective of this research is to classify the BrCa images for newly generated images. The model performs preprocessing, segmentation, feature extraction, and classification. In preprocessing, a hybrid median filtering (HMF) is used to eliminate the noise in the images. The contrast of the images is enhanced using quadrant dynamic histogram equalization (QDHE). Then, ROI segmentation is performed using the USE-Net deep learning model. The CaffeNet model is used for feature extraction on the segmented images, and finally, classification is made using the improved random forest (IRF) with extreme gradient boosting (XGB). The model obtained 97.87% accuracy, 98.45% sensitivity, 95.24% specificity, 98.96% precision, and 98.70% f1-score for ultrasound images. The model gives 98.31% accuracy, 99.29% sensitivity, 90.20% specificity, 98.82% precision, and 99.05% f1-score for mammogram images.

Detection and Classification of Colorectal Polyp Using Deep Learning.

Colorectal Cancer (CRC) is the third most dangerous cancer in the world and also increasing day by day. So, timely and accurate diagnosis is required to save the life of patients. Cancer grows from polyps which can be either cancerous or noncancerous. So, if the cancerous polyps are detected accurately and removed on time, then the dangerous consequences of cancer can be reduced to a large extent. The colonoscopy is used to detect the presence of colorectal polyps. However, manual examinations performed by experts are prone to various errors. Therefore, some researchers have utilized machine and deep learning-based models to automate the diagnosis process. However, existing models suffer from overfitting and gradient vanishing problems. To overcome these problems, a convolutional neural network- (CNN-) based deep learning model is proposed. Initially, guided image filter and dynamic histogram equalization approaches are used to filter and enhance the colonoscopy images. Thereafter, Single Shot MultiBox Detector (SSD) is used to efficiently detect and classify colorectal polyps from colonoscopy images. Finally, fully connected layers with dropouts are used to classify the polyp classes. Extensive experimental results on benchmark dataset show that the proposed model achieves significantly better results than the competitive models. The proposed model can detect and classify colorectal polyps from the colonoscopy images with 92% accuracy.

Image Enhancement Algorithm of Electrowetting Display Based on Image Segmentation and Dynamic Histogram Equalization

Image Enhancement Algorithm of Electrowetting Display Based on Image Segmentation and Dynamic Histogram Equalization

Gradient, Texture Driven Based Dynamic-Histogram Equalization For Medical Image Enhancement

In many clinical diagnostic measurements, medical images play some significant role but often suffer from various types of noise and low-luminance, which causes some notable changes in overall system accuracy with misdiagnosis rate. To improve the visual appearance of object regions in medical images, image enhancement techniques are used as potential pre-processing techniques. Due to its simplicity and easiness of implementation, histogram equalization is widely preferred in many applications. But due to its mapping function based image transformation during enhancement process affect the biomedical patterns which are essential for diagnosis. To mitigate these issues in medical images, a new method based on gradient computations and Texture Driven based Dynamic histogram equalization (GTDDHE) is accomplished to increase the visual perception. The spatial texture pattern is also included to ensure the texture retention and associated control over its variations during histogram modifications. Experimental results on MRI, CT images, eyes images from medical image datasets and quantitative analysis by PSNR, structural similarity index measurement (SSIM), information entropy (IE) and validated that the proposed method offers improved quality with maximum retention of biomedical patterns across all types of medical images.

Triple Clipped Histogram-Based Medical Image Enhancement Using Spatial Frequency.

In this paper, a novel triple clipped histogram model-based fusion approach has been proposed to improve the basics features, brightness preservation and contrast of the medical images. This incorporates the features of the equalized image and input image together. In the initial step, the low-contrast medical image is equalized using the triple clipped dynamic histogram equalization technique for which the histogram of the input medical image is split into three sections on the basis of standard deviation with almost equal number of pixels. The clipping process of the histogram is performed on every histogram section and mapped to a new dynamic range using simple calculations. In the second step, the sub-histogram equalization process is performed separately. Approximation and detail coefficients of equalized and input images are separated using discrete wavelet transform (DWT). Thereafter, the approximation coefficients are modified using some basic calculation-based fusion which involves singular value decomposition (SVD) and its inverse. Detail coefficients are fused using spatial frequency features. This yields modified approximation and detail coefficients for an enhanced image. Finally, inverse discrete wavelet transform (IDWT) has been applied to the modified coefficients which result in an enhanced image with improved visual quality. These improvements are analyzed qualitatively and quantitatively.

A novel extended Kalman filter with support vector machine based method for the automatic diagnosis and segmentation of brain tumors

BackgroundBrain tumors are life-threatening, and their early detection is crucial for improving survival rates. Conventionally, brain tumors are detected by radiologists based on their clinical experience. However, this process is inefficient. This paper proposes a machine learning-based method to 1) determine the presence of a tumor, 2) automatically segment the tumor, and 3) classify it as benign or malignant. MethodsWe implemented an Extended Kalman Filter with Support Vector Machine (EKF-SVM), an image analysis platform based on an SVM for automated brain tumor detection. A development dataset of 120 patients which supported by Tiantan Hospital was used for algorithm training. Our machine learning algorithm has 5 components as follows. Firstly, image standardization is applied to all the images. This is followed by noise removal with a non-local means filter, and contrast enhancement with improved dynamic histogram equalization. Secondly, a gray-level co-occurrence matrix is utilized for feature extraction to get the image features. Thirdly, the extracted features are fed into a SVM for classify the MRI initially, and an EKF is used to classify brain tumors in the brain MRIs. Fourthly, cross-validation is used to verify the accuracy of the classifier. Finally, an automatic segmentation method based on the combination of k-means clustering and region growth is used for detecting brain tumors. ResultsWith regard to the diagnostic performance, the EKF-SVM had a 96.05% accuracy for automatically classifying brain tumors. Segmentation based on k-means clustering was capable of identifying the tumor boundaries and extracting the whole tumor. ConclusionThe proposed EKF-SVM based method has better classification performance for positive brain tumor images, which was mainly due to the dearth of negative examples in our dataset. Therefore, future work should obtain more negative examples and investigate the performance of deep learning algorithms such as the convolutional neural networks for automatic diagnosis and segmentation of brain tumors.

Image dehazing based on dark channel spatial stimuli gradient model and image morphology

Image dehazing has become a critical problem to cater to as it has several parameters that need to be addressed. Real color, contrast, and illumination are the major parameters that are to be restored in the dehazed image. Different scenarios distort these parameters in different ways so it is difficult to restore the original image. Many approaches are used in the literature to cater to these problems but suffer from low contrast, faded color, and weak edges. This article introduces an effective technique, which is named as dark channel spatial stimuli gradient model (DCSSGM) that performs well for the aforementioned problems. The DCSSGM technique applies the dark channel prior (DCP) and spatial stimuli gradient sketch model (SSGSM) on each color channel to eliminate the haze from the image and to restore true edges. SSGSM is responsible to restore robust edges in an image using the perceived brightness and calculations based on neighborhood similarity. Morphology is applied to the resultant image to receive sharp true edges. The final restored image is the output of dynamic histogram equalization (DHE) which restores the contrast of the image. The evaluation analysis qualitatively and quantitatively concludes that the DCSSGM technique outperforms other state-of-the-art image dehazing techniques.

A fuzzy and spline based dynamic histogram equalization for contrast enhancement of brain images

AbstractIn general, medical images acquired at insufficient lighting conditions are suffering from low contrast issues which are inadequate for image analysis steps. One standard solution to improve the image contrast is to change its intensity distribution with the help of an image histogram. The histogram‐based contrast enhancement methods treat the images as regions rather than objects which would be more useful for applications like brain image enhancement. In this paper, a Fuzzy and Spline based Histogram Equalization (FSDHE) is proposed to perform contrast enhancement with medical images. The proposed FSDHE method partitions the image into connected components, and the type of components are identified with a fuzzy membership function. The dynamic histogram equalization is applied to each component individually. The equalized sub‐histograms are combined to drive the global histogram, which is inconsistent as dynamic histogram equalization treated the intensity range for each connected component differently. Hence, a spline‐based histogram smoothing is proposed here in this research work. The equalized intensity mapping is received as control points for the polynomial curve, and a smooth intensity transformation is interpolated as a spline curve. The proposed FSDHE model is analyzed with MRI‐brain image dataset of 3064 images, which consists of both benign and malignant cases. The contrast enhancement performance of the proposed FSDHE method is quantified by various measures like Absolute Mean Brightness Error (AMBE), Peak Signal to Noise Ratio (PSNR), Contrast (C), Weber Contrast (WC), Entropy, Hausdorff Distance (HD) and Texture Preservation (TP) measures. The performance of the FSDHE method is compared against with other histogram equalization methods, and the results indicate that the FSDHE method achieves better quality measures of 3.1401, 30.5499, 21.5486, 0.7779, 4.0252, 0.2777, and 0.7836 for the seven‐performance metrics.

RETRACTED ARTICLE: Different classification methods of fundus image segmentation using quincunx wavelet decomposition

Retinal vessel in fundus image is segmented with a comprehensive method for classification. The method is processed in four phases namely, preprocessing, segmentation, features extraction and classification, this method can be used on different images sets. Retinal vessels are enhanced by brightness preserving dynamic fuzzy histogram equalization (BPDFHE), separating enhanced image is used to detect the retinal diseases. Then these enhanced images are segmented by using quincunx wavelet decomposition for extracting features like first order statistics and gray level co-occurrence matrix (GLCM). The feature vector encodes information to handle the normal and abnormal retinal image and those features are classified using different classifiers (Adaboost, DSVM, ELMASR, EPLS, KNN, NB, NBFFS, OCPLS, RBFN, RF, SOWA, SVM and SVNN) and the performance is evaluated in detail. Blood vessel segmentation with this method is effective for retinal image computational analyses such as early retinal disease detection. Experimental results on three public retinal data sets like DRIVE, STARE and MESSIDOR and real time images are taken from Agarwal’s Eye Hospital, Tirunelveli, demonstrating an excellent performance in comparison with retinal vessel segmentation methods reported recently.

A robust technique for copy-move forgery detection from small and extremely smooth tampered regions based on the DHE-SURF features and mDBSCAN clustering

ABSTRACT The keypoint-based copy-move forgery (CMF) detection is one of the most widely used CMF detection methods; however the keypoint-based CMF detection method cannot effectively and efficiently detect the small and extremely smooth tampered regions in the input image. A CMF detection method is proposed to tackle the above mention problem. In the proposed CMF detection method, the contrast of the input image is adjusted using the dynamic histogram equalization (DHE) method. A speeded-up robust feature (SURF) descriptor is used to extract features from the tampered image and matched using Euclidean distance. The novel modified density-based spatial clustering of application with noise (mDBSCAN) clustering technique is applied to the matched features to generate the binary mask followed by the detection of CMF regions. Three standard datasets, MICC-F220, MICC-F2000, and CoMoFoD, are used to evaluate the proposed CMF detection method performance. The experimental results indicate that the proposed CMF detection method outshines the state-of-the-art CMF detection method in terms of precision (P) and recall (R).

Dynamic Histogram Equalization for contrast enhancement for digital images

Magnetic Resonance Imaging (MRI) is an efficient tool, produced by applying radio waves and magnetic fields which is being useful in the diagnosis of various diseases like cancer, epilepsy and stroke etc. The quality of the resulting image is needed to be enhanced because it is challenging for the specialists to investigate. Modified Histogram Equalization on Fuzzy based Improved Particle Swarm Optimization (FIPSO) is proposed for Dynamic Histogram Equalization which resolves this problem through image contrast enhancement. The details of an images are captured by smoothing and it uses Gaussian function to distribute pixel intensity to nearest pixel. It uses normal distribution and here blur is removed by applying Non subsampled Contourlet Transform. Then local maxima are calculated to extract dark and bright pixel values. The smoothed images are fuzzified with TSK (Takagi-Sugeno-Kang) model and it provides importance to all the local maxima intervals. An Improved particle swarm optimization (IPSO) algorithm is obtained by combining Galactic Swarm Optimization (GSO) with PSO which equalizes histogram of an image. FIPSO algorithm is used to the minimum contrast images of MRI brain images. Non-subsampled Contourlet transform (NSCT) based modified histogram equalization enhances image contrast. Here IPSO generates optimum values and these value are used to calculate cumulative distribution function in histogram equalization. The quality measures demonstrate that the current equalization technique attains highest performance against existing techniques in terms of brightness and contrast.

Contrast enhancement by intensity preservation‐based dynamic histogram equalization for electrowetting display

Due to the phenomenon of contact angle saturation and charge trapping, contrast of the electrowetting display will be decreased, and the details of the image will be lost. A histogram equalization algorithm is commonly used to improve the image contrast enhancement. Therefore, in order to improve the display quality of the electrowetting display, this article proposes an extension of dynamic histogram equalization (DHE) techniques, called intensity preservation‐based dynamic histogram equalization (IPDHE). Initially, the proposed IPDHE technique separates the histogram into four subhistograms based on intensity preservation. Then, the resultant subhistograms are reshaped by the nonzero bins as the amount of emphasis given on total pixels. Mathematically, the intensity preservation measure of each subhistogram stands for the density and boosted level of the subhistogram. Finally, each subhistogram is equalized. Simulation results show that, for the dataset of test images, the proposed algorithm successfully enhances the contrast of images while preserving the maximum average information content (entropy), presents more image details, and applies to the electrowetting display. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

A Noninvasive System for the Automatic Detection of Gliomas Based on Hybrid Features and PSO-KSVM

Due to their location, malignant brain tumors are one of humanity's greatest killers, among these tumors, gliomas are the most common. The early detection of gliomas can contribute to the design of proper treatment schemes and, thus, improve the survival rate of patients. However, it is a challenging task to detect the gliomas within the complex structure of the brain. The conventional artificial diagnosis is time-consuming and relies on the clinical experience of radiologists. To detect gliomas more efficiently, this paper proposes a noninvasive automatic diagnosis system for gliomas based on the machine learning methods. First, image standardization, including size normalization and background removal, is applied to produce standard images; then, the modified dynamic histogram equalization is implemented to enhance the low-contrast standard brain images, and skull removal based on outlier detection is presented. Furthermore, hybrid features, including gray-level co-occurrence matrix, pyramid histogram of the oriented gradient, modified completed local binary pattern, and intensity-based features are extracted together from the enhanced images, and their dimensions are reduced by principal component analysis. Kernel support vector machine (KSVM) combined with the particle swarm optimization is eventually adopted to train classifiers; in this paper, brain magnetic resonance imaging images are labeled with normal, glioma, and other. The experimental results show that the accuracy, sensitivity, and specificity of the proposed method can reach 98.36%, 99.17%, and 97.83%, respectively, which indicates that the proposed method performs better than many current systems.

Variance Based Brightness Preserved Dynamic Histogram Equalization for Image Contrast Enhancement

This paper proposes a novel variant of Brightness Preserving Dynamic Histogram Equalization (BPDHE) having more brightness preserving capability with less computational time. This variant, called Variance based Brightness Preserve Dynamic Histogram Equalization (VBBPDHE) uses the interclass and intraclass variance information to segment out the histogram recursively. This variant does not need the smoothing operation of input histogram and also no need to compute local maxima or minima to segment out the histogram unlike BPDHE. Visual analysis, quality metrics and execution time clearly demonstrate the efficiency of the proposed VBBPDHE over well-known existing methods.

Linearly quantile separated weighted dynamic histogram equalization for contrast enhancement

Contrast-enhancement is a prerequisite of various image-processing and computer-vision applications. When we employ contrast-enhancement tools based on histogram equalization, the mean-shift problem commonly arises. In this work, we propose a new approach to contrast enhancement which overcomes the mean-shift problem. In the proposed approach, we suggest a quantile-based division of the histogram of a given image, which can be used across the entire spectrum of grey level for contrast-enhancement. Due to its non-recursive nature, the proposed approach is computationally less expensive than many other methods. Experimental results show that the proposed method performs better than other existing methods available in related literature. Further, this method preserves image brightness more accurately and takes less time as compared to prevailing state of art approaches.