Search Results (2,153)

Film-based cellulose nitrate negatives are a unique class of objects that contain important information about life, historical buildings, and the natural landscapes of past years. Increased sensitivity to storage conditions makes these objects highly flammable and can lead to irretrievable loss. In this regard, timely identification of the degradation process is a necessary step towards further conservation and restoration. This work studies the possibility of detecting the degradation process based on cellulose nitrate artifact yellowing. A total of 20 normal and 20 yellowed negatives from the collection of Karl Kosse (The State Museum and Exhibition Center ROSPHOTO) were selected as objects for statistical study. The novelty of this work is in its demonstration of the possibility to divide negatives into normal and yellowed areas with different shades based on different B/R and B/G ratios of both light and dark negatives, i.e., regardless of the distribution of RGB component values for the obtained digital photo from the negative. Moreover, the obtained differentiation result was demonstrated for individual image pixels, without the need for averaging over a certain area. Full article

Satellites frequently encounter atmospheric haze during imaging, leading to the loss of detailed information in remote sensing images and significantly compromising image quality. This detailed information is crucial for applications such as Earth observation and environmental monitoring. In response to the above issues, this paper proposes an end-to-end multi-scale adaptive feature extraction method for remote sensing image dehazing (MSD-Net). In our network model, we introduce a dilated convolution adaptive module to extract global and local detail features of remote sensing images. The design of this module can extract important image features at different scales. By expanding convolution, the receptive field is expanded to capture broader contextual information, thereby obtaining a more global feature representation. At the same time, a self-adaptive attention mechanism is also used, allowing the module to automatically adjust the size of its receptive field based on image content. In this way, important features suitable for different scales can be flexibly extracted to better adapt to the changes in details in remote sensing images. To fully utilize the features at different scales, we also adopted feature fusion technology. By fusing features from different scales and integrating information from different scales, more accurate and rich feature representations can be obtained. This process aids in retrieving lost detailed information from remote sensing images, thereby enhancing the overall image quality. A large number of experiments were conducted on the HRRSD and RICE datasets, and the results showed that our proposed method can better restore the original details and texture information of remote sensing images in the field of dehazing and is superior to current state-of-the-art methods. Full article

Most existing low-light image enhancement (LIE) methods are primarily designed for human-vision-friendly image formats, such as sRGB, due to their convenient storage and smaller file sizes. In addition, raw images provide greater detail and a wider dynamic range, which makes them more suitable for LIE tasks. Despite these advantages, raw images, the original format captured by cameras, are larger and less accessible and are hard to use in methods of LIE with mobile devices. In order to leverage both the advantages of sRGB and raw domains while avoiding the direct use of raw images as training data, this paper introduces sRrsR-Net, a novel framework with the image translation process of sRGB–raw–sRGB for LIE task. In our approach, firstly, the RGB-to-iRGB module is designed to convert sRGB images into intermediate RGB feature maps. Then, with these intermediate feature maps, to bridge the domain gap between sRGB and raw pixels, the RAWFormer module is proposed to employ global attention to effectively align features between the two domains to generate reconstructed raw images. For enhancing the raw images and restoring them back to normal-light sRGB, unlike traditional Image Signal Processing (ISP) pipelines, which are often bulky and integrate numerous processing steps, we propose the RRAW-to-sRGB module. This module simplifies the process by focusing only on color correction and white balance, while still delivering competitive results. Extensive experiments on four benchmark datasets referring to both domains demonstrate the effectiveness of our approach. Full article

This paper presents a Regeneration filter for reducing near Salt-and-Pepper (nS&P) noise in images, designed for selective noise removal while simultaneously preserving structural details. Unlike conventional methods, the proposed filter eliminates the need for median or other filters, focusing exclusively on restoring noise-affected pixels through localized contextual analysis in the immediate surroundings. Our approach employs an iterative processing method, where additional iterations do not degrade the image quality achieved after the first filtration, even with high noise densities up to 97% spatial distribution. To ensure the results are measurable and comparable with other methods, the filter’s performance was evaluated using standard image quality assessment metrics. Experimental evaluations across various image databases confirm that our filter consistently provides high-quality results. The code is implemented in the R programming language, and both data and code used for the experiments are available in a public repository, allowing for replication and verification of the findings. Full article

Fibrous dysplasia is an uncommon bone disorder affecting various parts of the skeleton, often affecting facial and cranial bones. In this case, a 10-year-old patient was diagnosed with fibrous dysplasia of the ethmoid sinus at an early age. The patient has experienced nasal congestion, snores, and worsening nasal patency since 2019. A CT scan revealed an expansive proliferative lesion, likely from the frontal or ethmoid bone, protruding into the nasal cavity, ethmoid sinus, and right orbit. The tumor causes bone defects in the area of the nasal bone, leading to fluid retention in the peripheral parts of the right maxillary sinus. The patient’s parents decided not to undergo surgery to remove the diseased tissue and reconstruct the area, as it would be very extensive, risky, and disfiguring. The patient is being treated conservatively with an MRI, with a contrast performed approximately every six months and infusions of bisphosphonates. Despite the lesion’s size, the patient does not experience pain characteristic of dysplasia, and functions typically. Fibrous dysplasia of bone is a rare condition that presents with the most visually apparent manifestations, often mistaken for other bone conditions. Advanced diagnostic tools, like CT and MRI, are used to identify conditions affecting the ethmoid sinus more frequently. However, diagnostic errors often occur in imaging studies, leading to confusion. The most common period for clinical manifestations and diagnosis is around 10 years of age. The preferred approach in managing fibrous dysplasia involves symptomatic treatment, which can alleviate airway obstruction, restore normal globe position and visual function, and address physical deformities. Surgical intervention is recommended only for patients with severe functional impairment, progressive deformities, or malignant transformation. Full article

The integration of Internet of Medical Things (IoMT) technology has revolutionized healthcare, allowing rapid access to medical images and enhancing remote diagnostics in telemedicine. However, this advancement raises serious cybersecurity concerns, particularly regarding unauthorized access and data integrity. This paper presents a novel, user-friendly, visible watermarking scheme for medical images—Visual and User-Friendly Medical Image Watermarking Scheme (VUF-MIWS)—designed to secure medical image ownership while maintaining usability for diagnostic purposes. VUF-MIWS employs a unique combination of inpainting and data hiding techniques to embed hospital logos as visible watermarks, which can be removed seamlessly once image authenticity is verified, restoring the image to its original state. Experimental results demonstrate the scheme’s robust performance, with the watermarking process preserving critical diagnostic information with high fidelity. The method achieved Peak Signal-to-Noise Ratios (PSNR) above 70 dB and Structural Similarity Index Measures (SSIM) of 0.99 for inpainted images, indicating minimal loss of image quality. Additionally, VUF-MIWS effectively restored the ROI region of medical images post-watermark removal, as verified through test cases with restored watermarked regions matching the original images. These findings affirm VUF-MIWS’s suitability for secure telemedicine applications. Full article

To reduce bandwidth usage in communications, absolute moment block truncation coding is employed to compress cover images. Confidential data are embedded into compressed images using reversible data-hiding technology for purposes such as image management, annotation, or authentication. As data size increases, enhancing embedding capacity becomes essential to accommodate larger volumes of secret data without compromising image quality or reversibility. Instead of using conventional absolute moment block truncation coding to encode each image block, this work proposes an effective reversible data-hiding scheme that enhances the embedding results by utilizing the traditional set of values: a bitmap, a high value, and a low value. In addition to the traditional set of values, a value is calculated using arithmetical differential coding and may be used for embedding. A process involving joint neighborhood coding and logical differential coding is applied to conceal the secret data in two of the three value tables, depending on the embedding capacity evaluation. An indicator is recorded to specify which two values are involved in the embedding process. The embedded secret data can be correctly extracted using a corresponding two-stage extraction process based on the indicator. To defeat the state-of-the-art scheme, bitmaps are also used as carriers in our scheme yet are compacted even more with Huffman coding. To reconstruct the original image, the low and high values of each block are reconstructed after data extraction. Experimental results show that our proposed scheme typically achieves an embedding rate exceeding 30%, surpassing the latest research by more than 2%. Our scheme reaches outstanding embedding rates while allowing the image to be perfectly restored to its original absolute moment block truncation coding form. Full article

Ground-Penetrating Radar (GPR) has demonstrated significant advantages in the non-destructive detection of road structural defects due to its speed, safety, and efficiency. This paper proposes a three-dimensional (3D) reconstruction method for GPR images, integrating the back-projection (BP) imaging algorithm to accurately determine the size, location, and other parameters of road structural defects. Initially, GPR detection images were preprocessed, including direct wave removal and wavelet denoising, followed by the application of the BP algorithm to effectively restore the defect’s location and size. Subsequently, a 3D data set was constructed through interpolation, and the effective reflection data were extracted by using a clustering algorithm. This algorithm distinguished the effective reflection data from the background data by determining the distance threshold between the data points. The 3D imaging of the defect was then performed in MATLAB. The proposed method was validated using both gprMax simulations and laboratory test models. The experimental results indicate that the correlation between the reconstructed and actual defects was approximately 0.67, demonstrating the method’s efficacy in accurately achieving the 3D reconstruction of road structural defects. Full article

Accurate and efficient measurement of deposited droplets’ volume is vital to achieve zero-defect manufacturing in inkjet printed organic light-emitting diode (OLED), but it remains a challenge due to droplets’ featurelessness. In our work, coherence scanning interferometry (CSI) is utilized to measure the volume. However, the CSI redundant sampling and image degradation led by the sample’s transparency decrease the efficiency and accuracy. Based on the prior degradation and strong representation for context, a novel method, volume measurement via fringe distribution module (VMFD), is proposed to directly measure the volume by single interferogram without redundant sampling. Firstly, the 3D point spread function (PSF) for CSI imaging is modeling to relate the degradation and image. Secondly, the Zernike to PSF (ZTP) module is proposed to efficiently compute the aberrations to PSF. Then, a physics aberration restoration network (PARN) is designed to remove the degradation via the channel Transformer and U-net architecture. The long term context is learned by PARN and beneficial to restoration. The restored fringes are used to measure the droplet’s volume by constrained regression network (CRN) module. Finally, the performances on public datasets and the volume measurement experiments show the promising deblurring, measurement precision and efficiency. Full article

Borehole imaging technology is a critical means for the meticulous measurement of rock mass structures. However, the inherent issue of probe eccentricity significantly compromises the quality of borehole images obtained during testing. This paper proposes a method based on grayscale feature analysis for reverse positioning of imaging probes and image restoration. An analysis of the response characteristics of probe eccentricity was conducted, leading to the development of a grayscale feature model and a method for reverse positioning analysis. By calculating the error matrix using the probe’s spatial trajectory, this method corrects and restores grayscale errors caused by probe eccentricity in images. Quantitative analysis was conducted on the azimuthal errors in borehole images caused by probe eccentricity, establishing a method for correcting image perspective errors based on probe spatial-positioning calibration. Results indicate significant enhancement in the effectiveness and measurement accuracy of borehole images. Full article

Introduction: Non-compressible torso hemorrhage (NCTH) is a major cause of preventable mortality in trauma, particularly when immediate surgical intervention is not available. Resuscitative Endovascular Balloon Occlusion of the Aorta (REBOA) has emerged as a promising technique to control severe hemorrhaging and stabilize patients until definitive surgical care can be performed. Case Presentation: We report the case of a 45-year-old woman who sustained multiple traumatic injuries—including thoracic, pelvic, and aortic damage—after a fall from approximately 5 m in an apparent suicide attempt. She arrived at a secondary-level trauma center in profound hemorrhagic shock, unresponsive to standard resuscitation. Interventions: As the patient’s condition deteriorated to cardiac arrest, an emergent REBOA procedure was performed by emergency physicians. This intervention rapidly restored hemodynamic stability, enabling damage control resuscitation and safe transfer to a Level 1 Trauma Center for definitive surgical management, including thoracic endovascular aortic repair and splenectomy. Outcomes: After prolonged intensive care, the patient recovered sufficiently to be discharged for rehabilitation. This case illustrates the life-saving potential of early REBOA deployment in a non-surgical, resource-limited setting to bridge patients to definitive care. Conclusions: This case supports integrating REBOA into emergency trauma protocols, particularly in centers without immediate surgical capabilities. Further research is warranted to refine REBOA deployment strategies, balloon positioning, patient selection, and the role of imaging guidance. Full article

This paper presents a comprehensive study of the application of AI-driven inpainting techniques to the restoration of historical photographs of the Czech city Most, with a focus on restoration and reconstructing the lost architectural heritage. The project combines state-of-the-art methods, including generative adversarial networks (GANs), patch-based inpainting, and manual retouching, to restore and enhance severely degraded images. The reconstructed/restored photographs of the city Most offer an invaluable visual representation of a city that was largely destroyed for industrial purposes in the 20th century. Through a series of blind and informed user tests, we assess the subjective quality of the restored images and examine how knowledge of edited areas influences user perception. Additionally, this study addresses the technical challenges of inpainting, including computational demands, interpretability, and bias in AI models. Ethical considerations, particularly regarding historical authenticity and speculative reconstruction, are also discussed. The findings demonstrate that AI techniques can significantly contribute to the preservation of cultural heritage, but must be applied with careful oversight to maintain transparency and cultural integrity. Future work will focus on improving the interpretability and efficiency of these methods, while ensuring that reconstructions remain historically and culturally sensitive. Full article

This study investigates the microstructure of dental enamel following demineralization and re-mineralization processes, using DIAGNOdent scores and images obtained via scanning electron microscopy (SEM), atomic force microscopy (AFM), and microhardness (Vickers). The research evaluates the effects of two experimental hydrogels, Anti-Amelogenin isoform X (ABT260, S1) and Anti-Kallikrein L1 (K3014, S2), applied to demineralized enamel surfaces over periods of 14 and 21 days. The study involved 60 extracted teeth, free from cavities or other lesions, divided into four groups: a positive group (+), a negative group (−) and groups S1 and S2. The last three groups underwent demineralization with 37% phosphoric acid for 20 min. The negative group (−) was without remineralization treatment. The DIAGNOdent scores indicate that the S1 group treated with Anti-Amelogenin is more effective in remineralizing the enamel surface compared to the S2 group treated with Anti-Kallikrein. These findings were corroborated by SEM and AFM images, which revealed elongated hydroxyapatite (HAP) nanoparticles integrated into the demineralized structures. Demineralization reduced enamel microhardness to about 1/3 of a healthy one. Both tested hydrogels restored enamel hardness, with S1 being more effective than S2. Both peptides facilitated the interaction between the newly added minerals and residual protein binders on the enamel surface, thereby contributing to effective enamel restoration. Full article

The recycling of Carbon Fibre-Reinforced Polymers (CFRPs) is becoming increasingly crucial due to the growing demand for sustainability in high-performance industries such as automotive and aerospace. This study investigates the impact of two chemical recycling techniques, chemically assisted solvolysis and plasma-enhanced solvolysis, on the morphology and properties of carbon fibres (CFs) recovered from end-of-life automotive parts. In addition, the effects of fibre sizing are explored to enhance the performance of the recycled carbon fibres (rCFs). The surface morphology of the fibres was characterised using Scanning Electron Microscopy (SEM), and their structural integrity was assessed through Thermogravimetric Analysis (TGA) and Raman spectroscopy. An automatic analysis method based on optical microscopy images was also developed to quantify filament loss during the recycling process. Mechanical testing of single fibres and yarns showed that although rCFs from both recycling methods exhibited a ~20% reduction in tensile strength compared to reference fibres, the application of sizing significantly mitigated these effects (~10% reduction). X-ray Photoelectron Spectroscopy (XPS) further confirmed the introduction of functional oxygen-containing groups on the fibre surface, which improved fibre-matrix adhesion. Overall, the results demonstrate that plasma-enhanced solvolysis was more effective at fully decomposing the resin, while the subsequent application of sizing enhanced the mechanical performance of rCFs, restoring their properties closer to those of virgin fibres. Full article

In the rapidly advancing realm of mobile technology, under-display camera (UDC) systems have emerged as a promising solution for achieving seamless full-screen displays. Despite their innovative potential, UDC systems face significant challenges, including low light transmittance and pronounced diffraction effects that degrade image quality. This study aims to address these issues by examining degradation phenomena through optical simulation and employing a deep neural network model incorporating hybrid frequency–spatial domain learning. To effectively train the model, we generated a substantial synthetic dataset that virtually simulates the unique image degradation characteristics of UDC systems, utilizing the angular spectrum method for optical simulation. This approach enabled the creation of a diverse and comprehensive dataset of virtual degraded images by accurately replicating the degradation process from pristine images. The augmented virtual data were combined with actual degraded images as training data, compensating for the limitations of real data availability. Through our proposed methods, we achieved a marked improvement in image quality, with the average structural similarity index measure (SSIM) value increasing from 0.8047 to 0.9608 and the peak signal-to-noise ratio (PSNR) improving from 26.383 dB to 36.046 dB on an experimentally degraded image dataset. These results highlight the potential of our integrated optics and AI-based methodology in addressing image restoration challenges within UDC systems and advancing the quality of display technology in smartphones. Full article