Digital Image Enhancement Research Articles

APPLICATION OF THE ALGORITHM OF PARAMETRIC AND NON-PARAMETRIC CONFIDENCE INTERVALS IN PRE-PROCESSING IMAGE DATA

Digital image processing and enhancement is one of the most important and frequently used issues in many fields of image processing. When handling images or sending them over a particular channel, they are subject to certain noise and require filtering methods. In this paper, the parametric confidence interval algorithm was compared to the nonparametric confidence interval algorithm for processing the noisy images. The results showed that a nonparametric confidence interval algorithm is better at defining the external parameters of an image in terms of noise elimination and enhancement landmarks.

Image Processing in Improving the Scan Results of Identity Cards and Family Cards with Noise with the Median Filtering Method

In the digital era, identity documents such as Identity Cards and Family Cards play a crucial role in administration and public services. The process of scanning these documents often results in images with noise that can interfere with data accuracy and identification. This research aims to improve the quality of scanned images by using the median filtering method to reduce salt and pepper noise. Median Filtering was chosen for its ability to preserve edges and details in images, which is crucial for identity documents such as Identity Cards and Family Cards. In this study, the system developed to enhance scanned images was implemented and tested. Evaluation results show that the Median Filtering method is effective in removing noise without damaging the edges and fine details of the images. The evaluation indicates that this system is effective in producing images that meet the quality standards required for official administration and identification. This research is expected to contribute to the development of digital image enhancement technology for identity documents, thus improving accuracy and reliability in public services.

ChromaFusionNet (CFNet): Natural Fusion of Fine-Grained Color Editing

Digital image enhancement aims to deliver visually striking, pleasing images that align with human perception. While global techniques can elevate the image's overall aesthetics, fine-grained color enhancement can further boost visual appeal and expressiveness. However, colorists frequently face challenges in achieving accurate, localized color adjustments. Direct composition of these local edits can result in spatial color inconsistencies. Existing methods, including color style transfer and image harmonization, exhibit inconsistencies, especially at boundary regions. Addressing this, we present ChromaFusionNet (CFNet), a novel approach that views the color fusion problem through the lens of image color inpainting. Built on the Vision Transformer architecture, CFNet captures global context and delivers high-fidelity outputs, seamlessly blending colors while preserving boundary integrity. Empirical studies on ImageNet and COCO datasets demonstrate CFNet's superiority over existing methods in maintaining color harmony and color fidelity. Robustness evaluations and user studies have further validated the effectiveness of CFNet. In conclusion, CFNet introduces an innovative approach to seamless, fine-grained color fusion, paving the way for advancements in the domain of fine-grained color editing. Code and pretrained models are available at our project page: https://yidong.pro/projects/cfnet.

Multiscale Feature Attention Module based Pyramid Network for Medical Digital Radiography Image Enhancement

Multiscale Feature Attention Module based Pyramid Network for Medical Digital Radiography Image Enhancement

Detection of cracks in concrete using near-IR fluorescence imaging

Structural health monitoring of civil infrastructure is a crucial component of assuring the serviceability and integrity of the built environment. A primary material used in the construction of civil infrastructure is concrete, a material that is susceptible to cracking due to a variety of causes, such as shrinkage, creep, overloading, and temperature change. Cracking reduces the durability of concrete structures, as it allows deleterious environmental agents to penetrate the surface, causing such damage as corrosion of steel reinforcement and delamination of the concrete itself. Conventional crack detection techniques are limited in scope due to issues relating to pre-planning, accessibility, and the need for close proximity to the test surface. Contactless optical image monitoring techniques offer the opportunity to overcome these limitations and have the potential to detect cracks at a distance. Concrete has been reported to have a near-infrared (Near-IR) fluorescence line at a wavelength of 1140 nm when excited with red light. This work investigates the use of fluorescence imaging for the detection of cracks in cementitious surfaces using shallow angle incidence excitation red light. Light oriented at a shallow angle does not excite interior surfaces of cracks, which appear as darker features in images of fluorescing concrete. Artificial cracks with widths of 0.2–1.5 mm were readily imaged using a near-IR camera at distances of 0.5 and 1.3 m. An additional concrete sample with a 0.08 mm wide crack was produced using a flexure apparatus and was also imaged. It is worth noting that the 0.08 mm crack was detected despite its width being below the 0.1 mm pixel resolution of the camera, with the aid of digital image enhancement algorithms.

Digital enhancement of OCT images toward better diagnostic of precursor cancer lesions in the uterine colon

We present a combination of image processing operations, which improve the quality of OCT images. The sequence is: (1) Gaussian blur, (2) Enhance Local Contrast (CLAHE), (3) FFT based band-pass filter, (4) Background Subtraction, (5) adjustment of the contrast of the image with light saturation (Enhance Contrast) and finally (6) reassignment of the palette such as to enhance the bright part of the images. The multi-step treatment reduces pixel-to-pixel noise, improves the homogeneity of the contrast, supresses vertical strips, reduces cosmetic distractions, equalizes contrast across a collection of images, and uses the pallet to emphasise areas of greater intensity. The enhanced images show a significant improvement in their quality, with details that do not appear in the original OCT image. The processing of these images and the comparison with untreated OCT images are shown throughout the paper.

Enhancement of Digital Radiographic Images for Gas Turbine Blades Based on Simple Scattering Model

Enhancement of Digital Radiographic Images for Gas Turbine Blades Based on Simple Scattering Model

Digital image enhancement by brightness and contrast manipulation using Verilog hardware description language

A foggy environment may cause digitally captured images to appear blurry, dim, or low in contrast. This will impact computer vision systems that rely on image information. With the need for real-time image information, such as a plate number recognition system, a simple yet effective image enhancement algorithm using a hardware implementation is very much needed to fulfil the need. To improve images that suffer from low exposure and hazy, the hardware implementations are usually based on complex algorithms. Hence, the aim of this paper is to propose a less complex enhancement algorithm for hardware implementation that is able to improve the quality of such images. The proposed method simply combines brightness and contrast manipulation to enhance the image. In order to see the performance of the proposed method, a total of 100 vehicle registration number images were collected, enhanced, and evaluated. The evaluation results were compared to two other enhancement methods quantitatively and qualitatively. Quantitative evaluation is done by evaluating the output image using peak signal-to-noise ratio and mean-square error evaluation metrics, while a survey is done to evaluate the output image qualitatively. Based on the quantitative evaluation results, our proposed method outperforms the other two enhancement methods.

Lineament mapping of structural-trend analysis over the Niger-Benue River-confluence and its environs, a case study of North-Central Nigeria

Evaluation, analysis and interpretation of aeromagnetic and satellite imagery data acquired over the Nigeria confluence and environs; Northcentral Nigeria were carried out in order to characterise the structural framework of the study area. Fourier domain of digital filtering tools and reduction to magnetic equator (RTE) operations were achieved to enhance the available data’s results. The geomagnetic field of the earth was removed from the aeromagnetic data using the IGRF- 12 model. Results from the 2D radially average power spectrum of the enhanced magnetic data estimate the depth of the magnetic sources as 20m. Several digital image enhancement techniques such as edge enhancement and general contrast stretching were applied to the satellite imagery after which the lineaments extracted from the magnetic data and images were mapped out on-screen using ArcMap 10.8. The RTE map showed a high magnetic intensity values ranging from 33051. 93 nT to 33165.3 nT and trend approximately E -W direction at the eastern and northwestern parts. The digital elevation model (DEM) of the confluence and environs was used to enhance geomorphic features. The structural lineaments orientation obtained from the analysis showed that the primary structural lineaments bearing is NE – SW, ENE -WSW and NNW – SSE and the structural directions derived from the 2D maps are aligned along NE – SW and NW – SE. This conforms to the general trend of structures produced by the Pan African events.

Digital image enhancement may improve sensitivity of cholesteatoma detection during endoscopic ear surgery.

This study investigates the possible benefits and limitations of the digital image enhancement systems provided by Storz Professional Image Enhancement System (SPIES) during endoscopic ear surgery (EES) for cholesteatoma. An increased detection of cholesteatoma residuals during the final steps of endoscopic surgery using DIE technology was hypothesized. Cross-sectional study. Tertiary referral hospital. A total of 10 questionnaires of 18 intraoperative pictures with equal numbers of cholesteatoma and non-cholesteatoma images, each presented in three different image-enhancing modalities (Clara, Spectra A, Spectra B), were generated. Fifty-one experienced ear surgeons participated to the survey and were randomly assigned to a questionnaire and completed it at two time points. The experts were asked to rate for each picture whether cholesteatoma was present or not. The answers were compared with the histopathological reports. Clara showed the highest accuracy in cholesteatoma detection, followed by Spectra A and lastly Spectra B. In contrast, Spectra B showed the highest sensitivity and Clara the highest specificity, while Spectra A was placed in the middle for both values. Using the Spectra B modality, most responses agreed across the two time points. Ear surgeons assessed the usefulness, as well as preference among image modalities for cholesteatoma surgery, in the following order: Clara, Spectra B, Spectra A. Digital enhancement technologies are applicable to EES. After complete cholesteatoma removal, Spectra B showed the highest sensitivity in the detection of cholesteatoma residuals as compared with Clara and Spectra A. Thus, Spectra B may be recommended to avoid missing any cholesteatoma residuals during EES.

Evaluating possible prehistoric cave art in the central Mediterranean: Analyses of pigment traces and identification of taphonomic processes at Għar Ħasan, Malta

The archaeology of the Maltese Islands, particularly the megalithic ‘temple’ structures and underground mass burial sites (hypogea) of the ‘Temple Period’ (ca. 3800 – 2300 BCE), has been the subject of considerable interest. Less is known about the prehistoric use of caves. In particular reports of prehistoric cave art at Għar Ħasan (Hasan’s Cave) have remained controversial, and have been argued to reflect an otherwise unknown pre-Neolithic occupation on the islands.Here, we report a reinvestigation of Għar Ħasan. To distinguish graffiti that have accumulated over the last century and possible older rock art, a range of non-invasive methods were used to determine the chemical properties and colour spectrum of the pigment traces: portable energy-dispersive X-ray fluorescence spectrometry (p-ED-XRF) and visible (VIS) and near-infrared (NIR) spectroscopy. In addition, pigment traces were analyzed with digital microscopy and image enhancement techniques (DStretch).Most of the previously reported cave paintings were shown to have been produced in the last century. One key panel may be older, but vandalism and other destructive processes make a definitive assessment challenging. While we therefore consider the evidence for ‘Palaeolithic’ cave art at Għar Ħasan to be inconclusive, our study also identified complex taphonomic processes that pose severe limitations on the identification of pigment via p-ED-XRF and spectroscopy and can inform and guide future initiatives for rock art conservation.

Contrast-Controllable Image Enhancement Based on Limited Histogram

To address the technical shortcomings of conventional histogram equalization (HE), such as over-enhancement and artifacts, we propose a histogram-constrained and contrast-tunable HE technique for digital image enhancement. Firstly, the input image histogram is partitioned into two parts, the main histogram and the constrained histogram, by a cumulative probability density threshold; second, the main histogram is redistributed equally in the whole grayscale range; and finally, the nonlinearity of the constrained histogram is mapped to the main histogram. The experimental averages show that the values of the two metrics, information entropy and MS-SSIM, processed by the algorithms in this paper, are more accurate compared to the other six excellent algorithms.

Mathematical insights into the original Retinex algorithm for image enhancement.

The Retinex theory, originally developed by Land and McCann as a computation model of the human color sensation, has become, with time, a pillar of digital image enhancement. In this area, the Retinex algorithm is widely used to improve the quality of any input image by increasing the visibility of its content and details, enhancing its colorfulness, and weakening, or even removing, some undesired effects of the illumination. The algorithm was originally described by its creators in terms of a sequence of image processing operations and was not fully formalized mathematically. Later, works focusing on aspects of the original formulation and adopting some of its principles tried to frame the algorithm within a mathematical formalism: this yielded every time a partial rendering of the model and resulted in several interesting model variants. The purpose of the present work is to fill a gap in the Retinex-related literature by providing a complete mathematical formalization of the original Retinex algorithm. The overarching goals of this work are to provide mathematical insights into the Retinex theory, promote awareness of the use of the model within image enhancement, and enable better appreciation of differences and similarities with later models based on Retinex principles. For this purpose, we compare our model with others proposed in the literature, paying particular attention to the work published in 2005 by Provenzi and others.

Digital x-ray medical image enhancement method based on human visual characteristics

Digital x-ray medical image with human visual characteristics is a kind of medical image formed by x-ray irradiation. Currently, medical image processing algorithm is the main hot topic. With the popularization of medical images in clinical diagnosis and the development of computer technology, the accuracy of clinical diagnosis can be greatly improved using medical image processing technology. For digital x-ray imaging in medical system, it is inevitable that the quality of medical image will be degraded by various factors, so it needs to be processed before they can be used. At the same time, with the development of medical digital x-ray detector technology, the range of medical images used will be much smaller than that of detectors. The medical digital x-ray image data received after detection also need to be processed by an effective image-processing algorithm before display. On the other hand, the research, analysis, and diagnostic use of computer in medical images can appropriately reduce the work intensity of doctors or medical staff, and also play an important role in the discovery of certain diseases. A method of digital x-ray medical image enhancement based on human visual characteristics is studied by analyzing the characteristics of histogram of x-ray image and the maximum entropy fuzzy threshold image enhancement algorithm. The experimental results show that the method can fully recognize the medical x-ray images with incorrect exposure, enhancing the effect of digital x-ray imaging and improving the recognition rate.

Basketry Shields of the Prehispanic Southwest

Indigenous American shield-making traditions are best known among the peoples of the Plains and Southwest cultural provinces, where shields were used in martial and ceremonial contexts. In these regions, shields are frequently represented in images cross-cutting a range of visual media including rock and mural paintings, and pictographs and petroglyphs, some of which exhibit considerable antiquity. Actual shields, however, are almost unknown archaeologically. This article presents new data resulting from an analysis of five coiled basketry shields recovered from archaeological sites in the northern Southwest. Digital image enhancement clarifies the nature of early shield decoration, while evidence for use in combat contributes to knowledge of shield evolution and function. Improved dating suggests the possibility that basketry shields predate the proliferation of shield imagery in the AD 1200s. These observations help reorient discussion of shield form, function, and iconography within the context of wider cultural developments during the AD 1200s and beyond.

Visual Art Design of Digital Works Guided by Big Data

With the rapid development of digital technology, the development speed of digital media is also relatively fast. Digital media technology has a great impact on people’s lifestyles and aesthetic concepts, and it also has a greater impact on visual art, creative thinking communication methods, and expression methods. In this study, the quality enhancement of digital images has been intensively studied based on the guidance of big data of eye-movement gaze points. A large amount of visual data are collected from public social resources, and the optimization research of image sensory quality is carried out in-depth using the acquired big data. Next, the region of interest (ROI) is obtained by combining the data with a two-dimensional Gaussian distribution model-fitting method, and the obtained data clustered and improved based on the K-means clustering algorithm to obtain ROI fixation points. Finally, discontinuities in the choice of sharpness in graphics and video playback are pointed out, and the final fixation data analysis is utilized. Results show that targeted optimization is very effective in improving the quality of digital images and saving space, enabling users to enjoy higher-quality visual digital images. The proposed method can be used to improve the dynamic resolution of images and videos.

New prehistoric rock art from the Caucasus? Results of the study of the Damirgaya rock shelter in Georgia

This paper presents the results of the multidisciplinary study carried out at the Damirgaya rock art site located in Georgia, southern Caucasus. The research has aimed to conduct archaeological excavation and obtain contextual data based on multidisciplinary investigations including geology, photogrammetry, digital image enhancement, archaeological survey of the surrounding landscape, etc. Archaeological excavation has demonstrated that the shelter does not include any in situ layers, although some interactions are still observed. Detailed examination of the paintings has revealed almost 70 paintings in contrast with the previously reported 20 due to limited access to the site in the past. Zoomorphic, geometric, and anthropomorphic motifs, excluding modern graffiti, represent Schematic rock art and based on regional comparisons and a small number of lithic tools, are suggested to belong to the prehistoric period, but more reliable dating is required.

Overview on remote sensing image scene classification using deep learning

Now a days, deep learning is becoming very famous dueto its power fulability of learning. Deep learning has been applied for remote sensing image analysis tasks including scene classification. Remote sensing has seen an enormous increase in the enhancement of digital images captured from satellites that cover almost all angle of the surface of the earth. This growth in data has forced the community of the remote sensing to apply deep learning algorithms to solve different remote sensing tasks. Convolutional neural networks have obtained huge success in the field of scene classification. The current best scene classification approaches are still far from achieving the human’s ability to classify scene types with a few labelled samples. This study provides a systematic survey of deep learning methods for remote sensing image scene classification by covering mover than 9papers.Inthispaper, we provide a brief discussion on Convolutional neural networks. Here we also discussed few-shot learning algorithm to overcome the gap between the current understanding level of machine and human-level performance.

Improved revealing of hidden structures and defects for historic art sculptures using poisson image editing

Radiography is a non-destructive tool and offers the acquisition of detailed information on the internal features of sculptures as a cultural heritage. However, radiographs contain different levels of blurriness mainly caused by the detection of scattered X-rays. Reduction of image blurriness provides improved contrast in targeted areas which enhances the extraction of information from the selected regions and features of the radiographs. In this study, we applied a set of convolution methods to a group of radiographic images of historic sculptures. Radiographs of the objects were provided with the associated documentation from the collection of the Radiographic Inspection Laboratory of the Universitat Politècnica de València. The selection of the particular objects was based on the difference in the materials used in their construction i.e. the objects were made of wood, paper, or wax. The Poisson Image Editing (PIE) based on L 2 -norm was applied for image enhancement of digital radiography images. The results showed that the PIE method was effective in selective region enhancement of the radiographic image contrast enabling better visualization of the objects’ internal structures. The application of the implemented algorithm enabled the conservators and radiographers involved in the study to improve the visualization of the sculptures' internal features and defects enhance the defects’ evaluation.

How is the Use of Digital Dental Radiology in a Developing Country? An Overview of Brazil

The aim of this study was to assess the use of digital dental radiology in Brazil, by focusing on the use of image receptors, imaging exams and digital image enhancement tools, also assessing the methods of professional image transfer. Questionnaires were distributed in person on dental meetings and digitally via messaging (WhatsApp®) and mailings list. The sample of this cross-sectional study consisted of 478 questionnaires. Most participants were woman (n=315, 65.9%), with average age of 33.8±9.2 years. Descriptive and frequency analysis was performed. Chi-square and Fisher’s exact tests were used (α=0.05). Most dentists worked at shared dental clinics (34.7%) and use digital image receptors (51.1%), but a representative percentage (48.9%) still exclusively use radiographic films. Photostimulable phosphor plate is the most used digital image receptor. Among extraoral exams, panoramic radiography (PAN) is the most used. Regarding dental specialties, oral radiologists and oral and maxillofacial surgeons mostly use cone-beam computed tomography (p<0.001). Most dentists who use digital systems make use of digital image enhancement tools (87.8%), mainly contrast, zoom, brightness and measurements. The most common method of professional image transfer (professional-professional and professional-patiens) is by email, with few dentists using online app and social media (26%). Therefore, while most Brazilian dentists use digital imaging systems, a significant percentage still exclusively use radiographic films. The most extraoral imaging exams used is PAN. Regarding image enhancement tools, brightness and contrast adjustments, zoom and measurements are the most applied. Finally, dentists generally use email for professional image transfer.