Image Contrast Enhancement Technique Research Articles

A Fuzzy Minkowski Distance-based fusion of convolutional neural networks for gastrointestinal disease detection

Accurate classification of gastrointestinal (GI) conditions from medical images is a critical task for facilitating timely diagnosis and effective treatment. However, the reliance on manual diagnosis introduces the possibility of human errors. In response, researchers have been tirelessly working to develop robust computerized methods that can significantly enhance diagnostic accuracy. In this study, we present a novel approach designed to elevate the precision of classification tasks through the utilization of a Fuzzy Minkowski Distance-based Ensemble Model. Our ensemble model is accurately constructed by integrating three well-established pre-trained convolutional neural network (CNN) models: MobileNet, ResNet101V2, and Xception. To enhance the robustness of these base models, we incorporate ResNeXt block, effectively amplifying their ability in feature extraction and representation. Furthermore, we extract probabilities from these models and aggregate them using a fuzzy Minkowski Distance approach. This technique serves to minimize error values between observed and ground-truth data, leading to a further enhancement in detection accuracy. For our experiments, we utilize a publicly available dataset containing 6000 endoscopy images. These images are not only enhanced through the application of image contrast enhancement techniques but are also subjected to augmentation processes, effectively enhancing the dataset's diversity and contributing to improved performance in the classification task. After conducting comprehensive assessments, we highlight the strength of our ensemble model compared to single base models and existing methods. The ensemble consistently achieves an impressive accuracy of 99.62%, showcasing its potential as a powerful tool for accurate diagnosis in the field of gastroenterology.

Face-based Gender Classification Using Deep Learning Model

Gender classification is a critical task in computer vision. This task holds substantial importance in various domains, including surveillance, marketing, and human-computer interaction. In this work, the face gender classification model proposed consists of three main phases: the first phase involves applying the Viola-Jones algorithm to detect facial images, which includes four steps: 1) Haar-like features, 2) Integral Image, 3) Adaboost Learning, and 4) Cascade Classifier. In the second phase, four pre-processing operations are employed, namely cropping, resizing, converting the image from(RGB) Color Space to (LAB) color space, and enhancing the images using (HE, CLAHE). The final phase involves utilizing Transfer learning, a powerful deep learning technique that can be effectively employed to Face gender classification using the Alex-Net architecture. The performance evaluation of the proposed gender classification model encompassed three datasets: the LFW dataset, which contained 1,200 facial images. The Faces94 dataset contained 400 facial images, and the family dataset had 400. The Transfer Learning with the Alex-Net model achieved an accuracy of 98.77% on the LFW dataset. Furthermore, the model attained an accuracy rate of 100% on both the Faces94 and family datasets. Thus, the proposed system emphasizes the significance of employing pre-processing techniques and transfer learning with the Alex-Net model. These methods contribute to more accurate results in gender classification. Where, the results achieved by applying image contrast enhancement techniques, such as HE and CLAHE, were compared. CLAHE achieved the best facial classification accuracy compared to HE.

Cetacean Optimization Based Medical Image Contrast-Enhancement Technique for Improving Disease Diagnosis in Cardiac MRI

Conventional preprocessing methods in medical imaging can effectively reduce the combination of Gaussian and Impulsive noise, while still maintaining the crucial features such as edges within the images. However, these techniques are found to lag in the optimizing the certain image parameters which play a crucial rule in enhancing the input images and thus may lead to failing in preserving the edges that may adversely affect the early diagnosis of diseases. An attempt has been made to enhance the contrast of cardiac MR (Medical Resonance) images using Cetacean Optimization Algorithm (COA) for improving disease diagnosis. Input cardiac images have been acquired from the free online available databases viz., SCMR consensus and AMRG Atlas. The methodology includes integration of the cost function with contrast measure based on certain metrics to create a newer transformation function. It results in enhanced search pattern using a transformation function in spatial domain and improves the pixel intensity to increase the resolution of an image. The developed algorithm’s performance was evaluated by computing various parameters including peak signal-to-noise ratio (PSNR), structural similarity index measurement (SSIM), mean square error (MSE), and normalized absolute error (NAE). Analysis of the results reveals that the proposed method achieves the highest PSNR (72 dB) and SSIM (0.99) values, along with the lowest NAE (0.16%) and MSE (0.22%) compared to both the particle swarm optimized texture-based histogram equalization (PSOTHE) method and the modified sunflower optimization (MSFO) method. The results indicate that the suggested strategy might improve the intricate details of the input cardiac MRI images. This enhancement can contribute to the efficient identification of cardiac disorder-specific biomarkers.

Quantifying the Impact: Evaluating Image Contrast Enhancement Methods in Modern Applications

In the realm of digital image processing, enhancing contrast is a fundamental step in improving visual quality and aiding in subsequent analysis tasks. This research paper delves into a comprehensive evaluation of contemporary image contrast enhancement techniques, aiming to provide a nuanced understanding of their efficacy in modern applications. The study scrutinizes six prominent methods, including Histogram Equalization, Probability and Statistics-based Segmentation, DCT-based Compression, Fourier Transforms, Image Restoration and Denoising, and Integration Techniques. Each technique is dissected, highlighting its mathematical foundation, crucial parameters, and experimental setup. The comparative analysis encompasses considerations of computational cost, user-friendliness, versatility, and potential synergies between methods. The findings illuminate the diverse strengths and limitations of each approach, empowering practitioners to make informed choices based on specific image processing requirements. This research contributes a comprehensive framework for quantifying the impact of image contrast enhancement techniques,

A Comprehensive Analysis of Deep Learning-Based Approaches for Prediction and Prognosis of Infectious Diseases.

Artificial intelligence is the most powerful and promising tool for the present analytic technologies. It can provide real-time insights into disease spread and predict new pandemic epicenters by processing massive amount of data. The main aim of the paper is to detect and classify multiple infectious diseases using deep learning models. The work is conducted by using 29,252 images of COVID-19, Middle East Respiratory Syndrome Coronavirus, Pneumonia, normal, Severe Acute Respiratory Syndrome, tuberculosis, viral pneumonia, and lung opacity which has been collected from various disease datasets. These datasets are used to train the deep learning models such as EfficientNetB0, EfficientNetB1, EfficientNetB2, EfficientNetB3, NASNetLarge, DenseNet169, ResNet152V2, and InceptionResNetV2. The images have been initially graphically represented using exploratory data analysis to study the pixel intensity and find anomalies by extracting the color channels in an RGB histogram. Later, the dataset has been pre-processed to remove noisy signals using image augmentation and contrast enhancement techniques. Further, feature extraction techniques such as morphological values of contour features and Otsu thresholding have been applied to extract the feature. The models have been evaluated on the basis of various parameters, and it has been discovered that during the testing phase, the InceptionResNetV2 model generated the highest accuracy of 88%, best loss value of 0.399, and root mean square error of 0.63.

A Novel Industrial Image Contrast Enhancement Technique Based on an Improved Ant Lion Optimizer

This work introduces an improved ant lion optimizer (ALO), called BIALO, for industrial images. BIALO employs three strategies to improve the performance of the original ALO. First, a novel inertial weight is used to modify the ALO to better balance exploration and exploitation during the process of searching the best solutions. Second, the local search part of the bat algorithm plays an important role in accelerating the algorithm convergence rate. Additionally, the ALO is integrated with invasive weed optimization algorithm to further improve the searching precision. The proposed BIALO is applied to industrial image enhancement, where it acts as an efficient tool that searches for the best parameters in a local/global enhancement transformation. To test the performance of BIALO, we compare it with other metaheuristic algorithms, such as the genetic algorithm, particle swarm optimization, flower pollination algorithm, grasshopper optimization algorithm and the original ALO, on some benchmark industrial images. The experimental results establish that BIALO is able to achieve better outcomes than those of the other algorithms.

Implementation of adaptive image contrast enhancement technique for Image Processing Applications

An automatic and objective assessment of image quality is important in processing of imaging data. To magnify the consumed medicine for simpler X-ray interpretation Digital Image Processing Techniques can be used. In this paper, the Implementation of adaptive image contrast enhancement technique for image processing applications is done. The main objective of the project is to produce image processing algorithm which can enhance the contrast information efficiently. The adaptive technique of selection for contrast has become effective in image processing applications. However, many of the imaging parameters are weighted towards late stage that allow detection such as passive contrast improvement across a compromised areas.

Fuzzy Mapped Histogram Equalization Method for Contrast Enhancement of Remotely Sensed Images

Histogram equalisation (HE) is a widely used image contrast enhancement technique which is less computationally complex, but it fails to preserve the brightness and natural appearance of the remotely sensed images. To overcome these limitations several modifications have been reported in the literature. However, the images processed by most of the methods still suffer with the problems of saturation artifacts and un-even expansion of intensities. This paper proposes a novel fuzzy mapped HE method to overcome the aforementioned limitations by partitioning the histogram into multiple segments, expanding each segment to full dynamic range using fuzzy mapping function, then equalising each segment independently, and finally normalising the combination of equalised segments. The normalisation process is controlled by a non-negative control factor, which requires a training data for its estimation. Experimental results demonstrate that the application of proposed method in the area of remote sensing yields better results than the contemporary methods.

Exposure-Based Energy Curve Equalization for Enhancement of Contrast Distorted Images

This paper presents a novel image contrast enhancement technique that uses exposure-based energy curve equalization (ECE) with a plateau limit. In a primary deviation from the current histogram equalization process for contrast enhancement, the proposed approach uses an energy curve for the same. The energy curve is computed based on the modified Hopfield neural network architecture, which contains spatial context information. The calculated energy curve is clipped with a plateau limit computed as the average of the energy curve. The exposure threshold is computed and used to divide the clipped energy curve. The two resulting energy curves are equalized independently, and the final enhanced image is generated by integrating the images achieved by transforming the equalized energy curves. The performance of the proposed method is evaluated on a variety of low contrast images. The subjective and objective evaluations of the proposed method are compared with the various histogram equalization (HE) based methods and other state-of-the-art methods to exemplify the effectiveness.

A logarithmic law based histogram modification scheme for naturalness image contrast enhancement

In this paper, a new logarithmic histogram modification technique for image contrast enhancement with naturalness preservation has been proposed. Traditional histogram equalization scheme usually causes extreme contrast enhancement, which results in unnatural look and artifacts. The proposed technique first enhances the contrast of the image globally through addition and logarithmic law based modification scheme, thereafter the local details of the image are emphasized through the coefficient scaling directly in the compressed domain using discrete cosine transformation. The proposed method can enhance the image contrast uniformly with less number of parameters without losing its basic features. Experimental results show that the proposed method preserves the natural appearance of the image and yields better perceptual quality as compared to the state-of-the-art techniques.

Combination of wavelet transform and singular value decomposition-based contrast enhancement technique for target detection in UAV reconnaissance thermal images

ABSTRACTIn Aerial surveillance, thermal images acquired by unmanned aerial vehicle (UAV) are greatly affected due to various external interferences, which results in a low contrast image. Widely used conventional contrast enhancement methods such as histogram equalization and dynamic range partitioning techniques suffer from severe brightness changes and reduced sharpness, which in turn fail to preserve the edge details of the image. Thus for efficient target detection, it is essential to develop effective thermal infrared image contrast and edge enhancement technique. In this paper, wavelet transform (WT) and singular value decomposition (SVD)-based image enhancement technique is attempted for the target detection using thermal images captured by UAV. The discrete wavelet transform (DWT), stationary wavelet transform (SWT) and SVD are used for texture feature enhancement, edge enhancement and illumination correction, respectively. The experimental results show that the proposed technique yields higher entropy (6.7485), EMEE (2.1212), MSSIM (0.8719) and lower AMBE (21.9049) values when compared to other existing techniques.

Image Contrast Enhancement by Homomorphic Filtering based Parametric Fuzzy Transform

In this paper, a new image contrast enhancement technique by taking the advantages of Homomorphic decomposition and fuzzy transform is been employed. For increasing the clarity of the image generally, histogram or Retinex theory based algorithms were used. These procedures worked on enhancing the reflectance layer by ignoring illumination, which is not a better strategy and leads to poor results. Fuzzy based image enhancement approach makes use of illumination by omitting reflectance. In our proposed algorithm, Homomorphic decomposition is used for getting the exact illumination image from the value layer of HSV (Hue, Saturation, Value) image. Next, the parametric fuzzy transform is employed to enhance the image by updating its membership functions and thereby smoothing the luminance layer. Finally, a weighted image is generated by pixel neighborhood property for preserving the image details. The results show the profoundness of the algorithm in terms of its clarity and complexity even for nonuniform illumination images.

A New Fuzzy Rule Based Contrast Enhancement Method using The Two-Steps Automatic Clustering Algorithm

The contrast is a major factor influencing the image quality; therefore, image contrast enhancement technique is more and more widely applied in the field of image processing. In this paper, a new fuzzy rule-based contrast enhancement method using the two-steps automatic clustering algorithm is proposed. Specifically, based on the Automatic clustering algorithm, a state-of-art method in cluster analysis and data mining, this paper proposes a two-steps Automatic clustering method to determine the number of fuzzy sets and locate the critical point in membership functions so that they are suitable for the distribution of pixel intensity values. The experiments on the "Lena" image and other natural images demonstrate that the new method can effectively enhance the contrast of the images and meet the demands of human eyes perception at the same time.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

A Combination of Variational Mode Decomposition and Histogram Equalization for Image Enhancement

A new technique for image contrast enhancement is proposed in this paper. The technique is based on the variational mode decomposition (VMD) of image and various histogram equalization approaches. The original image is decomposed into different sub-images or modes using VMD. The histogram equalization approaches such as the conventional histogram equalization, the contrast limited adaptive histogram equalization are applied to the mode1 of the original image. For obtaining the enhanced image, a weighting scheme is introduced to combine the processed mode1 image and the original image. The proposed technique is evaluated using various images including the medical image. The experimental results demonstrate that the proposed technique outperforms the existing methods for image enhancement. For medical images, the PSNR and the image quality index values improve by using the proposed technique over various HE approaches.

Image contrast enhancement techniques-a survey

Image Enhancement plays an essential role in a wide area of vision applications. Image enhancement is a technique used to enhance the qual-ity of the image such that it can be easily viewed by both men and machine.Contrast makes a visual difference that makes an object distin-guishable from background and other objects. The major goal of image contrast enhancement is to increase the visual quality of the image. In this research study, various image contrast enhancement techniques are reviewed. This research work also focuses on the comparative study of contrast enhancement techniques for identifying an effective contrast enhancement technique.

An Optimized Multiple Fuzzy Membership Functions based Image Contrast Enhancement Technique

Image enhancement is an emerging method for analyzing the images clearer for interpretation and analysis in the spatial domain. The goal of image enhancement is to serve an input image so that the resultant image is more suited to the particular application. In this paper, a novel method is proposed based on Mamdani fuzzy inference system (FIS) using multiple fuzzy membership functions. It is observed that the shape of membership function while converting the input image into the fuzzy domain is the essential important selection. Then, a set of fuzzy If-Then rule base in fuzzy domain gives the best result in image contrast enhancement. Based on a different combination of membership function shapes, a best predictive solution can be determined which can be suitable for different types of the input image as per application requirements. Our result analysis shows that the quality attributes such as PSNR, Index of Fuzziness (IOF) parameters give different performances with a selection of numbers and different sized membership function in the fuzzy domain. To get more insight, an optimization algorithm is proposed to identify the best combination of the fuzzy membership function for best image contrast enhancement.

Color Image Contrast Enhancement Based on Spatial Transformation and Expert Fuzzy Technique

The current image enhancement algorithms have the problems of color drift, difficulty of parameter selection, convergence guarantee, large amount of computation and poor real-time performance. The effect of image enhancement should mainly consider the visual perception of human eyes, and it is difficult to process images with accurate standards and methods. Therefore, the fuzzy technology based on imprecise concepts and expert experience is a favourable approach to solve the problems of current image enhancement algorithm. This paper proposed a color image contrast enhancement technique, firstly to hue, saturation and intensity of color space from RGB color space transform, then image enhancement processing would be implemented to the intensity component of the color image by using expert fuzzy technology. The proposed algorithm does not change the hue and saturation of the original color image. The contrast experiment shows that the proposed method has improved the visual effect of the image obviously. The amount of calculation is small and the real-time performance is well.

A Hybrid Face Image Contrast Enhancement Technique for Improved Face Recognition Accuracy

A Hybrid Face Image Contrast Enhancement Technique for Improved Face Recognition Accuracy

Một phương pháp mới nâng cao độ tương phản ảnh màu theo hướng tiếp cận trực tiếp

Image contrast enhancement techniques have two mainly methods: indirect method and direct method. While indirect methods only modify the histogram without defining any specific contrast measure, the direct methods establish a criterion of contrast measurement and enhance the image by improving the contrast measure. Among many direct methods, only the studies by Cheng and Xu modified the contrast at each point of grayscale image using a contrast measure [6, 7]. In this paper we propose a new method for enhancing the contrast of color images based on the direct method. The experimental results demonstrate that the combination of our proposed method with Fuzzy C_Mean (FCM) clustering algorithms performs well on different color images.

Histogram Interpolation Methods for Image Contrast Enhancement

Histogram equalization (HE) is a well-known technique for image contrast enhancement. However, HE frequently over-enhances the contrast of a given image to produce false contours in the enhanced image. One of the reasons for the production of false contours is the sparseness of the equalized histogram, which means that there exist gaps in continuous-tone. In this paper, we propose three methods for interpolating the sparse histogram obtained by HE, and utilize the interpolated histograms as the target ones for histogram specification or histogram matching, which transforms the histogram of a given image into the specified target histogram. Filling the gaps in equalized histogram by interpolation, we can reduce the occurrence of false contours. Experimental results show that the proposed methods can alleviate the over-enhancement of the contrast, which occurs frequently when we use HE. We also evaluate the quality of contrast-enhanced images by using three image quality measures, where the proposed methods achieve better performance than HE in many cases.