Contrast Enhancement Technique Research Articles

A deep learning approach for virtual contrast enhancement in Contrast Enhanced Spectral Mammography

Contrast Enhanced Spectral Mammography (CESM) is a dual-energy mammographic imaging technique that first requires intravenously administering an iodinated contrast medium. Then, it collects both a low-energy image, comparable to standard mammography, and a high-energy image. The two scans are combined to get a recombined image showing contrast enhancement. Despite CESM diagnostic advantages for breast cancer diagnosis, the use of contrast medium can cause side effects, and CESM also beams patients with a higher radiation dose compared to standard mammography. To address these limitations, this work proposes using deep generative models for virtual contrast enhancement on CESM, aiming to make CESM contrast-free and reduce the radiation dose. Our deep networks, consisting of an autoencoder and two Generative Adversarial Networks, the Pix2Pix, and the CycleGAN, generate synthetic recombined images solely from low-energy images. We perform an extensive quantitative and qualitative analysis of the model’s performance, also exploiting radiologists’ assessments, on a novel CESM dataset that includes 1138 images. As a further contribution to this work, we make the dataset publicly available. The results show that CycleGAN is the most promising deep network to generate synthetic recombined images, highlighting the potential of artificial intelligence techniques for virtual contrast enhancement in this field.

A new histogram equalization technique for contrast enhancement of grayscale images using the differential evolution algorithm

A new histogram equalization technique for contrast enhancement of grayscale images using the differential evolution algorithm

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.

Birefringence mapping of biological tissues based on polarization sensitive non-interferometric quantitative phase imaging technique

ObjectiveOral cancer is a leading cause of mortality globally, particularly affecting developing regions where oral hygiene is often overlooked. The optical properties of tissues are vital for diagnostics, with polarization imaging emerging as a label-free, contrast-enhancing technique widely employed in medical and scientific research over past few decades. Materials and methodsWe present a novel polarization sensitive quantitative phase imaging of biological tissues by incorporating the conventional polarization microscope and transport of intensity equation-based phase retrieval algorithm. This integration provides access to the birefringence mapping of biological tissues. The inherent optical anisotropy in biological tissues induces the polarization dependent refractive index variations which can provide the detailed insights into the birefringence characteristics of their extracellular constituents. Experimental investigations were conducted on both normal and cancerous oral tissue samples by recording a set of three polarization intensity images for each case with a step size of 2 μm. ResultsA noteworthy increment in birefringence quantification was observed in cancerous as compared to the normal tissues, attributed to the proliferation of abnormal cells during cancer progression.The mean birefringence values were calculated for both normal and cancerous tissues, revealing a significant increase in birefringence of cancerous tissues (2.1 ± 0.2) × 10−2 compared to normal tissues (0.8 ± 0.2) × 10−2. Data were collected from 8 patients in each group under identical experimental conditions. ConclusionThis polarization sensitive non-interferometric optical approach demonstrated effective discrimination between cancerous and normal tissues, with various parameters indicating elevated values in cancerous tissues.

A Novel Framework of Hybrid Optimization Techniques for Contrast-Enhancement in Cardiac MRI Medical Images

Medical image processing plays a crucial role in diagnosing and treating various diseases, with Magnetic Resonance Imaging (MRI) being a widely used modality. A previous study introduced an algorithm aimed at eliminating noise from MRI images through a filtering process, effectively suppressing mixed noise while preserving important structures like edges. However, limitations in optimizing parameters for image enhancement and occasional edge preservation failures were observed. Addressing the challenge of inadequate contrast enhancement in medical image processing, this study proposes a solution utilizing the hybrid cetacean optimization algorithm and Sand Cat Swarm Optimization (COA-SCSO) to enhance contrast in MRI images. The proposed approach focuses on improving the contrast nature in MRI images to mitigate image degradation during acquisition. Cardiac MRI images from SCMR consensus and AMRG Atlas databases are analyzed, emphasizing the consideration of spatial information for enhancement. The enhancement process involves integrating a cost function with a contrast measure based on multiple metrics to create a novel transformation function. Soft-computing approaches are employed to reduce time complexity, while expanding the search pattern through a transformation function in the spatial domain. This method enhances pixel intensity, contributing to increased image resolution. Performance evaluation of the proposed algorithm incorporates various parameters such as Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Normalized Absolute Error (NAE), and Structural Similarity Index (SSIM). Results from analysis of ten cardiac MRI images indicate that the proposed algorithm achieves the highest PSNR (98) and SSIM (0.99) values, as well as the lowest NAE (-0.17) and MSE (0.16) values, compared to the particle swarm optimized texture-based histogram equalization (PSOTHE) method and modified sunflower optimization (MSFO) method. Overall, the COA-SCSO approach demonstrates promising results in enhancing contrast in MRI images, offering potential benefits for medical diagnosis and treatment planning. Further research could explore additional optimization techniques and validation on larger datasets to further validate its effectiveness in clinical practice.

Estimating high-density aboveground biomass within a complex tropical grassland using Worldview-3 imagery

A large percentage of native grassland ecosystems have been severely degraded as a result of urbanization and intensive commercial agriculture. Extensive nitrogen-based fertilization regimes are widely used to rehabilitate and boost productivity in these grasslands. As a result, modern management frameworks rely heavily on detailed and accurate information on vegetation condition to monitor the success of these interventions. However, in high-density environments, biomass signal saturation has hampered detailed monitoring of rangeland condition. This issue stems from traditional broad-band vegetation indices (such as NDVI) responding to high levels of photosynthetically active radiation (PAR) absorption by leaf chlorophyll, which affects leaf area index (LAI) sensitivity within densely vegetative regions. Whilst alternate hyperspectral solutions may alleviate the problem to a certain degree, they are often too costly and not readily available within developing regions. To this end, this study evaluated the use of high-resolution Worldview-3 imagery in combination with modified NDVI indices and image manipulation techniques in reducing the effects of biomass signal saturation within a complex tropical grassland. Using the random forest algorithm, several modified NDVI-type indices were developed from all potential dual-band combinations of the Worldview-3 image. Thereafter, linear contrast stretching and histogram equalization were implemented in conjunction with Singular Value Decomposition (SVD) to improve high-density biomass estimation. Results demonstrated that both contrast enhancement techniques, when combined with SVD, improved high-density biomass estimation. However, linear contrast stretching, SVD, and modified NDVI indices developed from the red (630–690 nm), green (510–580 nm), and near-infrared 1 (770–895 nm) bands were found to produce the best biomass predictive model (R2 = 0.71, RMSE = 0.40 kg/m2). The results generated from this research offer a means to alleviate the biomass saturation problem. This framework provides a platform to assist rangeland managers in regionally assessing changes in vegetation condition within high-density grasslands.

Three-dimensional volume rendering ultrasound for assessing placenta accreta spectrum severity and discriminating it from simple scar dehiscence

Prenatal ultrasound discrimination between placenta accreta spectrum and scar dehiscence with underlying nonadherent placenta is challenging both prenatally and intraoperatively, which often leads to overtreatment. In addition, accurate prenatal prediction of surgical difficulty and morbidity in placenta accreta spectrum is difficult, which precludes appropriate multidisciplinary planning. The advent of advanced 3-dimensional volume rendering and contrast enhancement techniques in modern ultrasound systems provides a comprehensive prenatal assessment, revealing details that are not discernible in traditional 2-dimensional imaging. This study aimed to evaluate the use of 3-dimensional volume rendering ultrasound techniques in determining the severity of placenta accreta spectrum and distinguishing between placenta accreta spectrum and scar dehiscence with underlying nonadherent placenta. A prospective, cohort study was conducted between July 2022 and July 2023 in the fetal medicine unit of Dr Soetomo Academic General Hospital, Surabaya, Indonesia. All pregnant individuals with anterior low-lying placenta or placenta previa with a previous caesarean section who were referred with suspicion of placenta accreta spectrum were consented and screened using the standardised 2-dimensional and Doppler ultrasound imaging. Additional 3-dimensional volumes were obtained from the sagittal section of the uterus with a filled urinary bladder. These were analyzed by rotating the region of interest to be perpendicular to the uterovesical interface. The primary outcomes were the clinical and histologic severity in the cases of placenta accreta spectrum and correct diagnosis of dehiscence with nonadherent placenta underneath. The strength of association between ultrasound and clinical outcomes was determined. Multivariate logistic regression analyses and diagnostic testing of accuracy were used to analyze the data. A total of 70 patients (56 with placenta accreta spectrum and 14 with scar dehiscence) were included in the analysis. Multivariate logistic regression of all 2-dimensional and 3-dimensional signs revealed the 3-dimensional loss of clear zone (P<.001) and the presence of bridging vessels on 2-dimensional Doppler ultrasound (P=.027) as excellent predictors in differentiating scar dehiscence and placenta accreta spectrum. The 3-dimensional loss of clear zone demonstrated a high diagnostic accuracy with an area under the curve of 0.911 (95% confidence interval, 0.819-1.002), with a sensitivity of 89.3% (95% confidence interval, 78.1-95.97%) and specificity of 92.9% (95% confidence interval, 66.1-99.8%). The presence of bridging vessels on 2-dimensional Doppler demonstrated an area under the curve of 0.848 (95% confidence interval, 0.714-0.982) with a sensitivity of 91.1% (95% confidence interval, 80.4-97.0%) and specificity of 78.6% (95% confidence interval, 49.2-95.3%). A subgroup analysis among the placenta accreta spectrum group revealed that the presence of a 3-dimensional disrupted bladder serosa with obliteration of the vesicouterine space was associated with vesicouterine adherence (P<.001). Three-dimensional volume rendering ultrasound is a promising tool for effective discrimination between scar dehiscence with underlying nonadherent placenta and placenta accreta spectrum. It also shows potential in predicting the clinical severity with urinary bladder involvement in cases of placenta accreta spectrum.

Evaluation of filtering and contrast in X-ray and computerized tomography scan lung classification

Deep learning provides many convenient methods to help medical practitioners take informed decisions about diverse ailments. The goal of this project is to measure the effectiveness of filters and contrast enhancement techniques qualitatively and quantitatively in classifying lung scan images. Transfer deep learning was used to obtain the necessary results, with DenseNet 121 being the base model. Salt and pepper filter was used to introduce noise, and 3×3 mean and 5×5 mean with contrast limited adaptive histogram equalization (CLAHE) was used to minimize the effect of noise. All layers excluding the rearmost were frozen, and new dense and dropout layers were added to identify features of computerized tomography (CT) scan images of lungs. The resultant models were of comparable accuracy, where the model with no filter gave the accurate results for the given data, and the one using the 5×5 mean filter gave better adaptability in classification of unseen data. The misclassification between normal and pneumonia affected lungs is relatively higher, because of the lack of distinct features between them.

A GPU scheme for multi-secret visual sharing with varied secret dimensions and contrast enhancement using blind super-resolution

Multi-secret visual sharing schemes are essential for secure and efficient sharing of sensitive visual information. However, existing schemes often overlook important considerations such as varied secret dimensions, contrast enhancement, and computational efficiency. This research addresses these challenges by proposing a comprehensive approach that addresses these limitations. Firstly, the scheme takes into account the varied dimensions of secret images encountered in real-world scenarios, allowing flexibility in sharing and reconstructing images of different sizes and aspect ratios. Secondly, the research integrates contrast enhancement techniques, such as blind super-resolution, to improve the visual quality and visibility of shared secret images affected by factors like noise, compression, or low lighting conditions. Lastly, to enhance the computational efficiency, the scheme leverages the power of Graphics Processing Units (GPUs) for parallel computing, enabling faster processing of large-scale image operations. The proposed scheme achieves outstanding results, with a high PSNR of 98.264, a strong NCC value of 0.965, an exceptionally low NAE value of 0.046, and an impressive SSIM value of 0.983. Furthermore, the GPU implementation provides a remarkable overall speedup of 112×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$112\ imes$$\\end{document} for 256×256\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$256 \ imes 256$$\\end{document} color images.

A convolution neural network for rapid and accurate staging of breast cancer based on mammography

IntroductionBreast cancer (BC) has been one of the main reasons for women's deaths in the recent decade. This study hypothesizes that the deep features resulting from mammography staging can be used to determine tumor staging and lymph nodes of the sentinel lymph node (SLN) before surgery in BC patients. MethodsA retrospective study of female BC patients being treated. Pathological variables were collected from medical records, including age, tumor location, staging based on the American Joint Committee on Cancer (AJCC), pathological type, human epidermal growth factor receptor 2 (HER2) status, Progesterone Receptor (PR) status, and Estrogen Receptor (ER) status. The contrast enhancement technique with Contrast-Limited Adaptive Histogram Equalization (CLAHE) was applied to the studied data using MATLAB software. The images were first changed from color to gray in the preprocessing process, and then the images were changed from gray to color again. The regions of interest (ROI) of the primary tumor were isolated from other areas of breast tissue by two expert radiologists. Therefore, a proposed convolution neural network (CNN) model was applied. ResultsIn this study, 390 patients' information was used to analyze the data. In total, out of 390 patients, 300 had metastatic SLN involvement, and 390 had a tumor size greater than 1 mm. Performance evaluation of the proposed CNN staging was AUC 0.919 (95 % CI: 0.852,0.971) for the training group and AUC 0.877 (95 % CI: 0.805,0.949) for the validation group. ConclusionsThis study proposes a deep learning model based on CNN based on mammographic images, which has a suitable function for determining tumor staging and SLN metastasis.

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.

Optimal Agents for Visualizing Collagen Tissue Microarchitecture Using Contrast-Enhanced MicroCT.

Micro-computed tomography (microCT) is a common tool for the visualization of the internal composition of organic tissues. Collagen comprises approximately 25-35% of the whole-body protein content in mammals, and the structure and arrangement of collagen fibers contribute significantly to the integrity of tissues. Collagen type I is also frequently used as a key structural component in tissue-engineered and bioprinted tissues. However, the imaging of collagenous tissues is limited by their inherently low X-ray attenuation, which makes them indistinguishable from most other soft tissues. An imaging contrast agent that selectively alters X-ray attenuation is thus essential to properly visualize collagenous tissue using a standard X-ray tube microCT scanner. This review compares various contrast-enhanced techniques reported in the literature for MicroCT visualization of collagen-based tissues. An ideal microCT contrast agent would meet the following criteria: (1) it diffuses through the tissue quickly; (2) it does not deform or impair the object being imaged; and (3) it provides sufficient image contrast for reliable visualization of the orientation of individual fibers within the collagen network. The relative benefits and disadvantages of each method are discussed. Lugol's solution (I3K), phosphotungstic acid (H3PW12O40), mercury(II) chloride (HgCl2), and Wells-Dawson polyoxometalates came closest to fitting the criteria. While none of the contrast agents discussed in the literature met all criteria, each one has advantages to consider in the context of specific lab capabilities and imaging priorities.

Contrast Enhancement of Alzheimer’s MRI using Histogram Analysis

Contrast enhancement of MRI images frequently needs considerable pre-processing to provide accurate data for disease diagnosis and proper treatment. Enhancing the appearance of medical images becomes a difficult task owing to the uncertainty of the obtained image quality. In this study, Alzheimer’s MRI images are subjected to a contrast enhancement algorithm for easy diagnosis. A noise reduction and contrast enhancement technique for MRI images is discussed in this research. Histogram-based algorithms are used to solve the problems of de-noising and enhancing the contrast of images for identification of the infected region. The proposed method is based on contrast-limited adaptive histogram equalization (CLAHE) and the comparison with Histogram Equalization (HE). The suggested enhancement technique's performance can be evaluated using several metrics, including Structure Similarity Index Measure (SSIM), and Peak Signal-to-Noise Ratio (PSNR). Observational studies revealed that the suggested approach is significantly more efficient than the basic enhancement techniques such as HE.

Fast Local Laplacian Filter Based on Modified Laplacian through Bilateral Filter for Coronary Angiography Medical Imaging Enhancement

Contrast enhancement techniques serve the purpose of diminishing image noise and increasing the contrast of relevant structures. In the context of medical images, where the differentiation between normal and abnormal tissues can be quite subtle, precise interpretation might become challenging when noise levels are relatively elevated. The Fast Local Laplacian Filter (FLLF) is proposed to deliver a more precise interpretation and present a clearer image to the observer; this is achieved through the reduction of noise levels. In this study, the FLLF strengthened images through its unique contrast enhancement capabilities while preserving important image details. It achieved this by adapting to the image’s characteristics and selectively enhancing areas with low contrast, thereby improving the overall visual quality. Additionally, the FLLF excels in edge preservation, ensuring that fine details are retained and that edges remain sharp. Several performance metrics were employed to assess the effectiveness of the proposed technique. These metrics included Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Normalization Coefficient (NC), and Correlation Coefficient. The results indicated that the proposed technique achieved a PSNR of 40.12, an MSE of 8.6982, an RMSE of 2.9492, an NC of 1.0893, and a Correlation Coefficient of 0.9999. The analysis highlights the superior performance of the proposed method when contrast enhancement is applied, especially when compared to existing techniques. This approach results in high-quality images with minimal information loss, ultimately aiding medical experts in making more accurate diagnoses.

Automatic segmentation of mandibular canal using transformer based neural networks.

Accurate 3D localization of the mandibular canal is crucial for the success of digitally-assisted dental surgeries. Damage to the mandibular canal may result in severe consequences for the patient, including acute pain, numbness, or even facial paralysis. As such, the development of a fast, stable, and highly precise method for mandibular canal segmentation is paramount for enhancing the success rate of dental surgical procedures. Nonetheless, the task of mandibular canal segmentation is fraught with challenges, including a severe imbalance between positive and negative samples and indistinct boundaries, which often compromise the completeness of existing segmentation methods. To surmount these challenges, we propose an innovative, fully automated segmentation approach for the mandibular canal. Our methodology employs a Transformer architecture in conjunction with cl-Dice loss to ensure that the model concentrates on the connectivity of the mandibular canal. Additionally, we introduce a pixel-level feature fusion technique to bolster the model's sensitivity to fine-grained details of the canal structure. To tackle the issue of sample imbalance and vague boundaries, we implement a strategy founded on mandibular foramen localization to isolate the maximally connected domain of the mandibular canal. Furthermore, a contrast enhancement technique is employed for pre-processing the raw data. We also adopt a Deep Label Fusion strategy for pre-training on synthetic datasets, which substantially elevates the model's performance. Empirical evaluations on a publicly accessible mandibular canal dataset reveal superior performance metrics: a Dice score of 0.844, click score of 0.961, IoU of 0.731, and HD95 of 2.947mm. These results not only validate the efficacy of our approach but also establish its state-of-the-art performance on the public mandibular canal dataset.

Two-dimensional histogram-based reversible contrast enhancement using bi-histogram equalization

In recent years, the reversible data hiding with contrast enhancement (RDH-CE) technique has been proposed and developed significantly; it can perform CE for images and restore the host image losslessly. To improve the embedding rate, CE capability, and visual quality, this paper proposes a reversible CE scheme based on 2D histogram equalization, which maintains the image quality and low brightness distortion with fewer iterations and has a high embedding rate and CE capability. Specifically, to prevent image brightness distortion, the 2D histogram is separated to obtain two 2D sub-histograms before histogram shifting. The dimension with the highest embedding capacity is dynamically selected for the embedding operation in each iteration, and the two-sided histogram equalization and information embedding are performed on the separated sub-histograms. The proposed method is evaluated on two datasets, and the results show that the method has high embedding and CE capabilities with low iteration and good visualization.

Fusion based Image Enhancement Approach for Brain Tumor Detection

Magnetic Resonance Imaging (MRI), is a crucial technology used in the processing of medical images that provides insights into the anatomy of soft organs in the human body and helps in detecting brain tumors and spinal tumors. Despite advances in technology, most images have intrinsic drawbacks such as reduced contrast and brightness, and noise. Several contrast enhancement techniques are used such as, HE, BBHE, DSIHE, CLAHE, RMSHE, and their fusion, have been deployed on different MRI images to handle these problems. Metrics such as, entropy, PIQE and BRISQUE are used in the assessment of the results. Through the different fusion combinations, most prominent results are obtained from CLAHE-RMSHE fusion with an entropy value of 6.2516 and BRISQUE value of 40.14.

Development of Optimal Hyperparameter Tuning-Cycle GAN for Photo-realistic Face Age Progression Model

Face age progression aims to change an individual’s face from a provided face image to forecast how that image will look in the future. Face aging is gaining much attention in today’s environment, which needs better security and a touchless unique identification mechanism. Researchers are focused on creating face processing algorithms to address the difficulty of producing realistic aged faces for smart system applications over the earlier decades. In the literature, the two basic needs of face age progression, aging accuracy, and identity preservation are not thoroughly addressed. According to the extraordinary gains in image synthesis made by deep generative methods and their significant influence on a wide variety of practical applications such as identifying missing persons using entertainment, childhood images, and so on, face age progression/regression has reawakened attention. The majority of present techniques concentrate on face age progression and is beneficial and productive in learning the transition across age groups utilizing paired data, i.e., face images of the similar individual at various ages. Through the motivation of the important success attained by Generative Adversarial Networks (GANs), this paper uses tactics to implement the improved Cycle GAN-based intelligent face age progression model. Initially, the standard datasets for the face progression are gathered, and the face is detected using the Viola-Jones object detection algorithm. Then, the pre-processing of the facial image is performed by median filtering and contrast enhancement techniques. Once the image is pre-processed, the Hyperparameter Tuning-Cycle GAN (HT-Cycle GAN) is adopted for face age progression. As an improvement, the hyperparameters of the Cycle GAN are optimized or tuned by the modified Galactic Swarm Optimization (GSO), known as Best Fitness-based Galactic Swarm Optimization (BF-GSO). From the evaluation of statistical analysis, the similarity score of BF-GSO-HT-CycleGAN is 0.80%, 3.33%, and 2.86% higher than cGAN, CycleGAN, and Dubbed FaceGAN, respectively. Here, the Dubbed FaceGAN is the 2nd greatest network. Furthermore, compared to traditional models utilizing distinct standard datasets, the experimental findings show that the suggested technique attains efficiency, accuracy, and flexibility.

Adaptive Technique for Contrast Enhancement of Leading Vehicle Tracks

During movement in various unpaved terrain conditions, the track impressions left over by the leading vehicles provide guiding and safe routes in the area. The delineation of these tracks captured by the images can extend immense support for guidance in real time. These tracks that look like edges in coarse-resolution images take the shape of elongated areas in fine-resolution images. In such a scenario, the high pass and edge detection filters give limited information to delineate these tracks passing through different surroundings. However, the distinct texture of these tracks assists in the delineation of these tracks from their surroundings. Gray level co-occurrence matrix (GLCM) representing the spatial relation of pixels is employed here to define the texture. The authors investigated the influence of different resolutions on the distinguishability of these tracks. The study revealed that texture plays an increasing role in distinguishing objects as the image resolution improves. The texture analysis extended to investigate the track impressions left over by the leading vehicle brings out an ample scope in delineating these tracks. The measures could improve the track contrast even better than conventional techniques. To select the most optimal contrast enhancement measure in a given scenario, authors proposed a quantified measure of track index. An investigation is made on the difference-based track index (TI) representing the mean contrast value of the track vis-à-vis off-track areas. The results show an increase in the quantified contrast from 7.83 per cent to 29.06 per cent. The proposed technique highlights the image with the highest track contrast in a given scenario. The study can lead to onboard decision-making for the rut following vehicles moving in low-contrast terrain.