Digital Image Enhancement Research Articles

Digital Protection of Cultural Heritage Based on Web Technology

With the rapid development of digital technology, it has now been widely used for the digital protection of cultural heritage. The traditional cultural heritage digital protection method of information retrieval recall rate is low, and the processing time is long. Therefore, a new digital protection method of cultural heritage based on web technology is proposed. An improved four-layer architecture design pattern is adopted to plan a web-based digital protection platform for cultural heritage. The application service provider (ASP) combines SQL server and B/S architecture to develop a database platform and functional modules for direct dynamic management of the website model. Based on this, combined with web technology, the digital image of cultural heritage is reconstructed followed by computing the uncertainty probability of web visualization, completing the modeling metalanguage, and classification of network cultural heritage. The digital image enhancement of cultural heritage is based on HSV color space. In order to realize the digital protection of traditional cultural heritage, multifiltering technology is introduced into the process of brightness digital image extraction. It is evident from the experimental results that the proposed method has higher recall and lower error and can enhance the digital image of cultural heritage in a shorter time, which shows that the proposed method can effectively achieve digital protection of cultural heritage.

Enhancement of Digital Stereo Vision Images based on Histogram and Gamma Correction Strategy

Image contrast enhancement methods have been a topic of interest in digital image processing for various applications like satellite imaging, recognition, medical imaging, and stereo vision. This paper studies the technique for image enhancement utilizing Adaptive Histogram Equalization and Weighted Gamma Correction to cater radiometric condition and illumination variations of stereo image pairs. In the proposed method, the stereo pair images are segmented together with weighted distribution into sub-histograms supported with Histogram Equalization (HE) mapping or gamma correction and guided filtering. The experimental result shows the experimented techniques outperform compare with the original image in evaluation parameters of visual quality and image enhancement parameters via Entropy, MSE, and PSNR, etc. taken for performance analysis and validation.

Multifeature Contrast Enhancement Algorithm for Digital Media Images Based on the Diffusion Equation

This paper studies the processing of digital media images using a diffusion equation to increase the contrast of the image by stretching or extending the distribution of luminance data of the image to obtain clearer information of digital media images. In this paper, the image enhancement algorithm of nonlinear diffusion filtering is used to add a velocity term to the diffusion function using a coupled denoising model, which makes the diffusion of the original model smooth, and the interferogram is solved numerically with the help of numerical simulation to verify the denoising processing effect before and after the model correction. To meet the real-time applications in the field of video surveillance, this paper focuses on the optimization of the algorithm program, including software pipeline optimization, operation unit balancing, single instruction multiple data optimization, arithmetic operation optimization, and onchip storage optimization. These optimizations enable the nonlinear diffusion filter-based image enhancement algorithm to achieve high processing efficiency on the C674xDSP, with a processing speed of 25 posts per second for 640 × 480 size video images. Finally, the significance means a value of super pixel blocks is calculated in superpixel units, and the image is segmented into objects and backgrounds by combining with the Otsu threshold segmentation algorithm to mention the image. In this paper, the proposed algorithm experiments with several sets of Kor Kor resolution remote sensing images, respectively, and the Markov random field model and fully convolutional network (FCN) algorithm are used as the comparison algorithm. By comparing the experimental results qualitatively and quantitatively, it is shown that the algorithm in this paper has an obvious practical effect on contrast enhancement of digital media images and has certain practicality and superiority.

Sequential Model for Digital Image Contrast Enhancement

Aims: The manuscript aims at designing and developing a model for optimum contrast enhancement of an input image. The output image of model ensures the minimum noise, the maximum brightness and the maximum entropy preservation. Objectives: * To determine an optimal value of threshold by using the concept of entropy maximization for segmentation of all types of low contrast images. * To minimize the problem of over enhancement by using a combination of weighted distribution and weighted constrained model before applying histogram equalization process. * To provide an optimum contrast enhancement with minimum noise and undesirable visual artefacts. * To preserve the maximum entropy during the contrast enhancement process and providing detailed information recorded in an image. * To provide the maximum mean brightness preservation with better PSNR and contrast. * To effectively retain the natural appearance of an images. * To avoid all unnatural changes that occur in Cumulative Density Function. * To minimize the problems such as noise, blurring and intensity saturation artefacts. Methods: 1. Histogram Building. 2. Segmentation using Shannon’s Entropy Maximization. 3. Weighted Normalized Constrained Model. 4. Histogram Equalization. 5. Adaptive Gamma Correction Process. 6. Homomorphic Filtering. Results: Experimental results obtained by applying the proposed technique MEWCHE-AGC on the dataset of low contrast images, prove that MEWCHE-AGC preserves the maximum brightness, yields the maximum entropy, high value of PSNR and high contrast. This technique is also effective in retaining the natural appearance of an images. The comparative analysis of MEWCHE-AGC with existing techniques of contrast enhancement is an evidence for its better performance in both qualitative as well as quantitative aspects. Conclusion: The technique MEWCHE-AGC is suitable for enhancement of digital images with varying contrasts. Thus useful for extracting the detailed and precise information from an input image. Thus becomes useful in identification of a desired regions in an image.

A new unsupervised pseudo-siamese network with two filling strategies for image denoising and quality enhancement

Digital image noise may be introduced during acquisition, transmission, or processing and affects readability and image processing effectiveness. The accuracy of established image processing techniques, such as segmentation, recognition, and edge detection, is adversely impacted by noise. There exists an extensive body of work which focuses on circumventing such issues through digital image enhancement and noise reduction, but this work is limited by a number of constraints including the application of non-adaptive parameters, potential loss of edge detail information, and (with supervised approaches) a requirement for clean, labeled, training data. This paper, developed on the principle of Noise2Void, presents a new unsupervised learning approach incorporating a pseudo-siamese network. Our method enables image denoising without the need for clean images or paired noise images, instead requiring only noise images. Two independent branches of the network utilize different filling strategies, namely zero filling and adjacent pixel filling. Then, the network employs a loss function to improve the similarity of the results in the two branches. We also modify the Efficient Channel Attention module to extract more diverse features and improve performance on the basis of global average pooling. Experimental results show that compared with traditional methods, the pseudo-siamese network has a greater improvement on the ADNI dataset in terms of quantitative and qualitative evaluation. Our method therefore has practical utility in cases where clean images are difficult to obtain.

Transurethral resection of the urinary bladder : Status quo and outlook on new developments

Transurethral resection of the urinary bladder (TURB) is the standard intervention in the diagnostic workup and treatment of non-muscle invasive bladder cancer. In order to minimize cancer recurrence and potential complications, continuous technical development of TURB is of high clinical interest. Presentation of the current standards and discussion of technological changes. Analysis of the current guideline recommendations and literature research. The limitations of classic monopolar TURB is supplemented by new resection methods (enbloc) and technologies (bipolar and laser resection). Along with improved visualization through partially established technologies of photodynamic and digital image enhancement, there is potential for optimization regarding the likelihood of recurrences and complications as well as the histological quality of the resected material. Apositive impact on the oncological value and safety of TURB seems possible through the use of modern technologies. Further establishment up to evidence-based guideline recommendations are necessary.

Digital Image Enhancement and. Classification of Thermal Infrared Imagery for Monitoring- White-tailed Deer in Missouri Wildlife Game Habitat

Abstract

Image and video processing on mobile devices: a survey.

Image processing and computer vision on mobile devices have a wide range of applications such as digital image enhancement and augmented reality. While images acquired by cameras on mobile devices can be processed with generic image processing algorithms, there are numerous constraints and external issues that call for customized algorithms for such devices. In this paper, we survey mobile image processing and computer vision applications while highlighting these constraints and explaining how the algorithms have been modified/adapted to meet accuracy and performance demands. We hope that this paper will be a useful resource for researchers who intend to apply image processing and computer vision algorithms to real-world scenarios and applications that involve mobile devices.

Efficient method for real-time range enhancement of electro-optical imaging system

Electro-optical (EO) imaging sensors are employed for object detection, recognition, identification, and tracking. The observation range of EO systems is defined as the maximum distance at which a target of interest can be seen and perceived. The performance of these systems deteriorates under degraded weather conditions, which severely affects the image quality and thus the observation range of EO surveillance systems. An improved image quality results in an enhanced observation range. Image quality can be improved by applying suitable digital image enhancement techniques by considering target/background signature knowledge. Under bad weather conditions, these digital techniques become ineffective due to the presence of scattered background signals. To reduce the effect of scattering, optical methods are also used to mitigate degradation caused by the atmosphere. It is observed that the optical method alone cannot produce the desired result. Observation range enhancement in surveillance system is achieved using the proposed hybrid method, which utilizes the optimal combination of optical and digital techniques in real-time. The proposed method not only improves human visual perception but also reveals more image content for better image analysis. The performance of the proposed range enhancement technique is compared with the predicted observation range model. The processed images using the hybrid method show more than 30% observation range enhancement under bad weather conditions. In addition to the subjective evaluation, qualitative and quantitative evaluations of image quality achieved through the proposed method are also presented. It is observed that the proposed hybrid method significantly improves the observation range and image quality of EO sensors under degraded weather in real time.

Application of artificial intelligence using a convolutional neural network for detecting cholesteatoma in endoscopic enhanced images

ObjectiveWe examined whether artificial intelligence (AI) used with the novel digital image enhancement system modalities (CLARA+CHROMA, SPECTRA A, and SPECTRA B) could distinguish the cholesteatoma matrix, cholesteatoma debris, and normal middle ear mucosa, and observe the middle ear cavity during middle ear cholesteatoma surgery. MethodsA convolutional neural network (CNN) was trained with a set of images chosen by an otologist. To evaluate the diagnostic accuracy of the constructed CNN, an independent test data set of middle ear images was collected from 14 consecutive patients with 26 cholesteatoma matrix lesions, who underwent transcanal endoscopic ear surgery at a single hospital from August 2018 to September 2019. The final test data set included 58 total images, with 1‒5 images from each modality for each case. ResultsThe CNN required only 10 s to analyze more than 58 test images. Using SPECTRA A and SPECTRA B, the CNN correctly diagnosed 15 and 15 of 26 cholesteatoma matrix lesions, with a sensitivity of 34.6% and 42.3%, and with a specificity of 81.3% and 87.5%, respectively. ConclusionOur preliminary study revealed that AI and novel imaging modalities are potentially useful tools for identifying and visualizing the cholesteatoma matrix during endoscopic ear surgery. The diagnostic ability of the CNN is not yet appropriate for implementation in daily clinical practice, based on our study findings. However, in the future, these techniques and AI tools could help to reduce the burden on surgeons and will facilitate telemedicine in remote and rural areas, as well as in developing countries where the number of surgeons is limited.

Calcium pyrophosphate crystal size and characteristics

ObjectiveTo describe the characteristics of calcium pyrophosphate (CPP) crystal size and morphology under compensated polarized light microscopy (CPLM). Secondarily, to describe CPP crystals seen only with digital enhancement of CPLM images, confirmed with advanced imaging techniques. MethodsClinical lab-identified CPP-positive synovial fluid samples were collected from 16 joint aspirates. Four raters used a standardized protocol to describe crystal shape, birefringence strength and color. A crystal expert confirmed CPLM-visualized crystal identification. For crystal measurement, a high-pass linear light filter was used to enhance resolution and line discrimination of digital images. This process identified additional enhanced crystals not seen by raters under CPLM. Single-shot computational polarized light microscopy (SCPLM) provided further confirmation of the enhanced crystals’ presence. ResultsOf 932 suspected crystals identified by CPLM, 569 met our inclusion criteria, and 293 (51%) were confirmed as CPP crystals. Of 175 unique confirmed crystals, 118 (67%) were rods (median area 3.6 ​μm2 [range, 1.0–22.9 ​μm2]), and 57 (33%) were rhomboids (median area 4.8 ​μm2 [range, 0.9–16.7 ​μm2]). Crystals visualized only after digital image enhancement were smaller and less birefringent than CPLM-identified crystals. ConclusionsCPP crystals that are smaller and weakly birefringent are more difficult to identify. There is likely a population of smaller, less birefringent CPP crystals that routinely goes undetected by CPLM. Describing the characteristics of poorly visible crystals may be of use for future development of novel crystal identification methods.

Fractional derivative based Unsharp masking approach for enhancement of digital images

Image visual quality is severely degraded due to various environmental conditions, thus, leading to the loss in image details. Therefore, an image enhancement approach is required to improve the visual quality of images. In this paper, Unsharp Masking (UM) approach based on Riemann-Liouville (RL), Grunwald-Letnikov (GL), and Riesz fractional derivatives is proposed for the image enhancement. The fractional derivatives based UM approach sharpened the edges of an image while preserving its low and medium frequency details. Furthermore, the extra parameter of fractional derivative provides an additional degree of freedom, thus, increasing the effectiveness of the proposed approach. Extensive simulations carried out on several standard images of different sizes validated the performance of proposed approach in comparison to the existing techniques. The capability of the proposed approach is further confirmed by considering the test images with varying illumination conditions. Moreover, the comparative analysis performed in terms of quantitative measures such as Information Entropy (IE), Average Gradient (AG), Measure of Enhancement (EME), etc. confirmed that the proposed UM approach based on Riesz fractional derivative outperforms the existing state-of-the-art image enhancement techniques. Furthermore, the potential of the proposed approach is validated by considering its application in the medical images.

A Journey from basic Image Features to Lofty Human Intelligence in Content-based Image Retrieval: Motivation, Applications and Future Trends

Due to a remarkable increase in the complexity of the multimedia content, there is a cumulative enhancement of digital images both online and offline. For the purpose of retrieving images from a vast storehouse of images, there is an urgent requirement of an effectual image retrieval system and the most effective system in this domain is denoted as content-based image retrieval (CBIR) system. CBIR system is generally based on the extraction of basic image attributes like texture, color, shape, spatial information, etc. from an image. But, there exists a semantic gap between the basic image features and high-level human perception and to reduce this gap various techniques can be used. This paper presents a detailed study about the various basic techniques with an emphasis on different intelligent techniques like, the usage of machine learning, deep learning, relevance feedback, etc., which can be used to achieve a high level semantic information in CBIR systems. In addition, a detailed outline regarding the framework of a basic CBIR system, various benchmark datasets, similarity measures, evaluation metrics have been also discussed. Finally, solution to some research issues and future trends have also been given in this paper.

New Insights for Understanding the Structural Deformation Style of the Strike-Slip Regime along the Wadi Shueib and Amman-Hallabat Structures in Jordan Based on Remote Sensing Data Analysis

This paper presents new findings that contribute to the understanding of the deformational style of the Wadi Shueib Structure (WSS) and the Amman-Halabat Structure (AHS) and their relationship with the regional tectonic regime of the Dead Sea Transform Fault (DSTF). Our research utilized Landsat-8 OLI imagery for the automatic extraction of lineaments, and our lineament mapping was facilitated by processing and digital image enhancement using principal component analysis (PCA). Our data revealed a relatively higher density of lineaments along the extension of the major faults of the WSS and AHS. However, a relatively lower density of lineaments was shown in areas covered by recent deposits. Two major lineament trends were observed (NNE-SSW and NW-SE) in addition to a minor one (NE-SW), and most of these lineaments are parallel to the orientation of the WSS and AHS. We offer the supposition that the DSTF has merged into the major faults of the WSS and AHS. We further suppose that these faults were reactivated as a restraining bend composed of active strike-slip fault branches that developed due to the NNW-SSE-trending Dead Sea transpressional stress field. Depending on the relationship between the direction of the WSF and AHF strands and the regional tectonic displacement along the DSTF, thrust components are present on faults with horsetail geometry, and these movements are accompanied by folding and uplifting. Thus, the major faults of the WSS and AHS represent a contractional horsetail geometry with associated folding and thrusting deformation.

Contrast Enhancement Using Optimum Threshold Selection

In the recent era, a boom was observed in the field of information retrieval from images. Digital images with high contrast are sources of abundant information. The gathered information is useful in the precise detection of an object, event, or anomaly captured in an image scene. Existing systems do uniform distribution of intensities and apply intensity histogram equalization. These improve the characteristics of an image in terms of visual appearance. The problem of over enhancement and the increase in noise level produces undesirable visual artefacts. The use of Otsu's single threshold method in existing systems is inefficient for segmenting an image with multiple objects and complex background. Additionally, these are incapable to improve the yield of the maximum entropy and brightness preservation. The aforementioned limitations motivate us to propose an efficient statistical pipelined approach, the Range Limited Double Threshold Weighted Histogram Equalization (RLDTWHE). This approach is an integration of Otsu's double threshold, dynamic range stretching, weighted distribution, adaptive gamma correction, and homomorphic filtering. It provides optimum contrast enhancement by selecting the best appropriate threshold value for image segmentation. The proposed approach is efficient in the enhancement of low contrast medical MRI images and digital natural scene images. It effectively preserves all essential information recorded in an image. Experimental results prove its efficacy in terms of maximum entropy preservation, brightness preservation, contrast enhancement, and retaining the natural appearance of an image.

Noise Removal and Enhancement of Digital Mammographic Images for Visual Screening

Cancer is one of the most dangerous diseases that if not diagnosed at early stages can lead to death. Cancer is of different types and breast cancer is a known form of it that is prevailing around the world. It is found in almost 11% of the world women population in their life time. The early detection of this type of cancer is essential not only to reduce the life fatalities but also for the human race. Due to some drawbacks and negative impacts, it remains a fascinating concern to recover the pictorial eminence of mammograms at the initial stage by a robust method to examine the cancer in the breast. In this study, we have presented a method of noise destruction and image enrichment by utilizing wavelet transform. The proposed method consisted of three steps. Also, the preprocessing was employed to improve the local divergence in condensed areas in which the improvement parameter delivers the anticipated aspect enrichment. In the proposed method, the effect of artifacts has been solved that is one of the critical issues now a day, which produces during the analyzing of the mammographic image. The proposed algorithm gave best results as compared to the existing works for which less user adjustment parameters are required.

RETRACTED ARTICLE: A brain tumor image segmentation technique in image processing using ICA-LDA algorithm with ARHE model

In digital image processing, image segmentation is the key methodology which is to be used frequently. In digital image processing, noise reduction and enhancement techniques are plays as a vital role. Brain is major and major organ of the human body which is to be controlled by the nervous system. In this paper, we proposed a brain tumor image enhancement technique with the help of the ICA-LDA (independent component analysis-linear discriminate analysis algorithm with ARHE (adaptive region based histogram enhancement) model. Image fusion technique is to apply for combination of the two or more input image. In this paper, the weighted average technique is to be used for image fusion techniques. The noise reduction and enhancement techniques are to be applied in preprocessing stage. The adaptive median filter is to be used for preprocessing stage. The ARHE (adaptive region based histogram enhancement) model is to be used for enhancement present in the preprocessing stage. The feature extraction and the feature optimization have to be utilized with the ICA (independent component analysis). The LDA (linear discriminate analysis) is to be used for the classification techniques. Using this classifier which is to separate the abnormal and normal stages. When the brain tumor is denoted as abnormal case then the morphological based segmentation is to be done. The simulation and result shows the analysis of various parameters such as specificity, sensitivity, positive predictive value, negative predictive value, accuracy, precision, and recall.

Accuracy of digital image enhancement in detection of vertical and horizontal root fracture

Two-dimensional intraoral radiography is the most common tool for recognizing root fractures. Improving the quality of images by means of enhancement tools can increase the recognition power of them. The aim of this study is to evaluate the effect of digital image enhancement on vertical and horizontal root fractures (HRFs) diagnostic accuracy. In this in vitro study, 100 human extracted teeth, involving 50 mandibular premolars and 50 maxillary incisors, were investigated. In total, 25 premolar teeth were vertically fractured and other 25 sound teeth served as testing group. According to the verified methods, 25 incisor teeth were fractured and other 25 teeth of this group served as testing ones. Following, by using the charge-coupled device sensor, preapical digital images were recorded. The original images were altered using reverse-contrast and colorization enhancement tools. Two different observers independently investigated all of the images. Receiver operating characteristic analysis was used to calculate the area under the curve (AUC) and sensitivity and specificity of all images. Data analyzde using receiver operating characteristic (ROC) analysis. Two-ways variance analysis was used to assess differences in the values (P = 0.05 ). AUC and sensitivity and specificity related to the original, reverse-contrast, and colorized images were calculated (0.84, 0.64, 0.99), (0.84, 0.64, 0.96), and (0.82, 0.64, 0.92) respectively, for vertically root fractured images. AUC and sensitivity and specificity related to the original, reverse-contrast, and colorized images were calculated (0.49, 0.44, 0.56), (0.50, 0.44, 0.60), and (0.48, 0.48, 0.48), respectively, for horizontally root-fractured images. The results of the present study revealed that reverse-contrast and colorized enhancement filters cannot be used as critical methods in detecting in vitro vertical and HRF.

Mathematical Methods in Image Processing: A Survey

Image processing is a fundamental field that plays a crucial role in various applications such as medical imaging, remote sensing, and computer vision. Mathematical methods form the foundation of image processing algorithms, providing the framework for understanding image properties and designing efficient processing techniques. This survey provides a comprehensive overview of the mathematical methods used in image processing, including digital image representation, image enhancement, restoration, and segmentation. Advanced mathematical methods such as sparse representations, variational methods, Markov random fields, and deep learning techniques are also discussed, highlighting their significance in advancing the field of image processing. The applications of mathematical methods in various domains, including medical imaging, satellite image analysis, forensic image analysis, and industrial image processing, are explored. Finally, the survey discusses the challenges faced by researchers in image processing, such as computational complexity, big data management, interdisciplinary research trends, and emerging technologies, and suggests future directions for research in this area.

Defect Inspection Based on Segmentation and Defective Tracking in Radiographic Image

In this paper, automatic weld defect segmentation into the radiographic image non-destructive evaluation and testing, with orthogonal polynomials transformation-enhancement (OPT-E) is presented. This proposed system defect identification the given defect Radiographic image. In digital radiographic images, the unknown masses appear very light with weak edges, and hence image enhancement technique needs to be applied with transform domain and radiographic images of some illustrative weld deserts invent. The proposed scheme has three phases. In first phase, a radiographic image enhancement technique, which is performed by logarithmic common variance and enhancement factor, computed from the absolute value of the orthogonal polynomials transformation coefficient as principal parameters for increasing the energy of the masses in the digital radiographic image enhancement. In case of successful enhanced of image in addition to gradient estimation scheme is working to point the edges current, in the next phase. The consequential edge image is again applied with orthogonal polynomials. In the final phase, edge tracking are the salient features with angle based defect identification. Experimental is improved quality of images and high relative segmentation by OPT-E.