Missing Areas Research Articles

Symmetric Connected U-Net with Multi-Head Self Attention (MHSA) and WGAN for Image Inpainting

This study presents a new image inpainting model based on U-Net and incorporating the Wasserstein Generative Adversarial Network (WGAN). The model uses skip connections to connect every encoder block to the corresponding decoder block, resulting in a strictly symmetrical architecture referred to as Symmetric Connected U-Net (SC-Unet). By combining SC-Unet with a GAN, the study aims to reconstruct images more effectively and seamlessly. The traditional discriminators only differentiate the entire image as true or false. In this study, the discriminator calculated the probability of each pixel belonging to the hole and non-hole regions, which provided the generator with more gradient loss information for image inpainting. Additionally, every block of SC-Unet incorporated a Dilated Convolutional Neural Network (DCNN) to increase the receptive field of the convolutional layers. Our model also integrated Multi-Head Self-Attention (MHSA) into selected blocks to enable it to efficiently search the entire image for suitable content to fill the missing areas. This study adopts the publicly available datasets CelebA-HQ and ImageNet for evaluation. Our proposed algorithm demonstrates a 10% improvement in PSNR and a 2.94% improvement in SSIM compared to existing representative image inpainting methods in the experiment.

PRN: progressive reasoning network and its image completion applications

Ancient murals embody profound historical, cultural, scientific, and artistic values, yet many are afflicted with challenges such as pigment shedding or missing parts. While deep learning-based completion techniques have yielded remarkable results in restoring natural images, their application to damaged murals has been unsatisfactory due to data shifts and limited modeling efficacy. This paper proposes a novel progressive reasoning network designed specifically for mural image completion, inspired by the mural painting process. The proposed network comprises three key modules: a luminance reasoning module, a sketch reasoning module, and a color fusion module. The first two modules are based on the double-codec framework, designed to infer missing areas’ luminance and sketch information. The final module then utilizes a paired-associate learning approach to reconstruct the color image. This network utilizes two parallel, complementary pathways to estimate the luminance and sketch maps of a damaged mural. Subsequently, these two maps are combined to synthesize a complete color image. Experimental results indicate that the proposed network excels in restoring clearer structures and more vivid colors, surpassing current state-of-the-art methods in both quantitative and qualitative assessments for repairing damaged images. Our code and results will be publicly accessible at https://github.com/albestobe/PRN.

PS-Net: A Learning Strategy for Accurately Exposing the Professional Photoshop Inpainting.

Restoring missing areas without leaving visible traces has become a trivial task with Photoshop inpainting tools. However, such tools have potentially illegal or unethical uses, such as removing specific objects in images to deceive the public. Despite the emergence of many forensics methods of image inpainting, their detection ability is still insufficient when attending to professional Photoshop inpainting. Motivated by this, we propose a novel method termed primary-secondary network (PS-Net) to localize the Photoshop inpainted regions in images. To the best of our knowledge, this is the first forensic method devoted specifically to Photoshop inpainting. The PS-Net is designed to deal with the problems of delicate and professional inpainted images. It consists of two subnetworks: the primary network (P-Net) and the secondary network (S-Net). The P-Net aims at mining the frequency clues of subtle inpainting features through the convolutional network and further identifying the tampered region. The S-Net enables the model to mitigate compression and noise attacks to some extent by increasing the co-occurring feature weights and providing features that are not captured by the P-Net. Furthermore, the dense connection, Ghost modules, and channel attention blocks (C-A blocks) are adopted to further strengthen the localization ability of PS-Net. Extensive experimental results illustrate that PS-Net can successfully distinguish forged regions in elaborate inpainted images, outperforming several state-of-the-art solutions. The proposed PS-Net is also robust against some postprocessing operations commonly used in Photoshop.

Ancient Painting Inpainting with Regional Attention-Style Transfer and Global Context Perception

Ancient paintings, as a vital component of cultural heritage, encapsulate a profound depth of cultural significance. Over time, they often suffer from different degradation conditions, leading to damage. Existing ancient painting inpainting methods struggle with semantic discontinuities, blurred textures, and details in missing areas. To address these issues, this paper proposes a generative adversarial network (GAN)-based ancient painting inpainting method named RG-GAN. Firstly, to address the inconsistency between the styles of missing and non-missing areas, this paper proposes a Regional Attention-Style Transfer Module (RASTM) to achieve complex style transfer while maintaining the authenticity of the content. Meanwhile, a multi-scale fusion generator (MFG) is proposed to use the multi-scale residual downsampling module to reduce the size of the feature map and effectively extract and integrate the features of different scales. Secondly, a multi-scale fusion mechanism leverages the Multi-scale Cross-layer Perception Module (MCPM) to enhance feature representation of filled areas to solve the semantic incoherence of the missing region of the image. Finally, the Global Context Perception Discriminator (GCPD) is proposed for the deficiencies in capturing detailed information, which enhances the information interaction across dimensions and improves the discriminator’s ability to identify specific spatial areas and extract critical detail information. Experiments on the ancient painting and ancient Huaniao++ datasets demonstrate that our method achieves the highest PSNR values of 34.62 and 23.46 and the lowest LPIPS values of 0.0507 and 0.0938, respectively.

Reactivities of N-Nitrosamines against Common Reagents and Reaction Conditions.

The knowledge of the reactivity of N-nitrosamines (NSAs) with common organic reagents in synthesis is essential in determining their presence in pharmaceutical products, if formed and retained during synthesis. In this study, we carried out a comprehensive survey of the Reaxys database for all reactions in which the NSA functional group is consumed. Very different reactivities for different classes of NSAs, e.g., N,N-dialkylnitrosamines and N,N-diphenylnitrosamine, were identified, suggesting substrates which should be included in any future reactivity screening. A classification of NSAs based on their reactivities, and corresponding reagents and transformations, was drawn up based on the data. Furthermore, the survey identified missing areas in the reported reactivities of NSAs with different reagents. This led to an experimental reactivity screening of 8 commercial NSAs with common synthetic reagents in the Mirabilis tool for purge assessment. The results showed Na2S2O4 in 1 M aqueous NaOH at 50 °C to be highly effective at destroying NSAs without damaging other organic compounds.

Structure-Guided Image Inpainting Based on Multi-Scale Attention Pyramid Network

Current single-view image inpainting methods often suffer from low image information utilization and suboptimal repair outcomes. To address these challenges, this paper introduces a novel image inpainting framework that leverages a structure-guided multi-scale attention pyramid network. This network consists of a structural repair network and a multi-scale attention pyramid semantic repair network. The structural repair component utilizes a dual-branch U-Net network for robust structure prediction under strong constraints. The predicted structural view then serves as auxiliary information for the semantic repair network. This latter network exploits the pyramid structure to extract multi-scale features of the image, which are further refined through an attention feature fusion module. Additionally, a separable gated convolution strategy is employed during feature extraction to minimize the impact of invalid information from missing areas, thereby enhancing the restoration quality. Experiments conducted on standard datasets such as Paris Street View and CelebA demonstrate the superiority of our approach over existing methods through quantitative and qualitative comparisons. Further ablation studies, by incrementally integrating proposed mechanisms into a baseline model, substantiate the effectiveness of our multi-view restoration strategy, separable gated convolution, and multi-scale attention feature fusion.

IMPACT OF MENTAL HEALTH & WELL-BEING ON ACADEMIC PERFORMANCE OF UNIVERSITY STUDENTS

The study was conducted to investigate the impact of mental health and well-being on academic performance of university students. Quantitative research design was adopted for the study. The sample of study comprised 300 university students from both private and public sector universities. A random sampling technique was used to select the study sample. Data was collected by a self-constructed questionnaire designed on a 5-point Likert scale. Experts validated the tool's content. Data analysis was done through SPSS, version 22, including independent frequency distribution, sample t-test, ANOVA, and regression analysis. The results of study offer significant information that showed that mental health and well-being significantly impacted the academic performance of university students. Thus, based on conclusion of study, it was recommended that a great focus on judgment and improvement of missing areas of the students' mental health should be considered by universities, their faculties, and their parents. Moreover, this study may open new interdisciplinary horizons for the future researchers by merging education and psychology to consider the different issues in a tailor-made format.

Advancing image inpainting efficiency: An exploration of pixel and channel split operations

Deep learning-based image inpainting techniques have achieved unprecedented results using encoder–decoder structures to recover complex missing areas of an image. Recent inpainting models use additional information or networks (e.g., landmarks, edges, styles, and filters) to realize improved restoration performance, but at the cost of increased computational resources. To improve the relationship between inpainting performance and the number of model parameters, researchers have investigated efficient structural approaches such as recurrent and residual connection structures. However, these methods are difficult to apply in the general encoder–decoder structure. In this study, we explored the downsampling and upsampling operations associated with an encoder–decoder structure. We propose two novel split operations: the pixel-split operation (PSO) and channel-split operation (CSO). The proposed PSO transfers image features from high to low resolution with two dilation rate effects and a similar number of parameters as existing downsampling operations. Conversely, the proposed CSO increases the image resolution using only one-fourth the number of parameters of existing upsampling operations. The restoration performance and efficiency of the proposed model were evaluated in terms of five metrics on public datasets, e.g., the Places2 and CelebA datasets, to validate our proposed operations’ contribution to inpainting performance. We achieved state-of-the-art performance and reduced the size of the parameters by 20%. An ablation study was conducted to confirm the effect of each operation on the CelebA-HQ dataset. Results indicated that these split operations exhibit an advanced relationship between inpainting performance and optimization of the model parameters. The corresponding codes are available online (https://github.com/MrCAIcode/Split_operation_for_inpainting).

Game-based training to promote handwashing, handrub and gloving for hospital pharmacy operators

ObjectivesAdherence to handwashing, handrub and gloving procedures is mandatory for safe, aseptic drug compounding in hospital pharmacies. This study measured participants’ satisfaction and effectiveness of a game-based training tool (Handtastic...

Maskrenderer: 3D-infused multi-mask realistic face reenactment

We present a novel end-to-end identity-agnostic face reenactment system, MaskRenderer, that can generate realistic, high fidelity frames in real-time. Although recent face reenactment works have shown promising results, there are still significant challenges such as identity leakage and imitating mouth movements, especially for large pose changes and occluded faces. MaskRenderer tackles these problems by using (i) a 3DMM to model 3D face structure to better handle pose changes, occlusion, and mouth movements compared to 2D representations; (ii) a triplet loss function to embed the cross-reenactment during training for better identity preservation; and (iii) multi-scale occlusion, improving inpainting and restoring missing areas. Comprehensive quantitative and qualitative experiments conducted on the VoxCeleb1 test set, demonstrate that MaskRenderer outperforms state-of-the-art models on unseen faces, especially when the Source and Driving identities are very different.

Multi‐stage image inpainting using improved partial convolutions

AbstractIn recent years, deep learning models have dramatically influenced image inpainting. However, many existing studies still suffer from over‐smoothed or blurred textures when missing regions are large or contain rich visual details. To restore textures at a fine‐grained level, a multi‐stage inpainting approach is proposed, which applies a series of partial inpainting modules as well as a progressive inpainting module to inpaint missing areas from their boundaries to the centre successively. Some improvements are made on the partial convolutions to reduce artifacts like blurriness, which require a convolution kernel to contain known pixels more than a certain proportion. Towards photorealistic inpainting results, the intermediate outputs from each stage are used to compute the loss. Finally, to facilitate the training process, a multi‐step training is designed that progressively adds inpainting modules to optimize the model. Experiments show that this method outperforms the current excellent techniques on the publicly available datasets: CelebA, Places2 and Paris StreetView.

Tooth autotransplantation and resin composite reshaping as a multidisciplinary approach for treating dental agenesia: A 10-year follow-up.

This case report describes the multidisciplinary approach performed in a 9-year-old male patient with dental agenesia affecting teeth 21 and 22. Autotransplantation of the right upper second premolar with incomplete rhizogenesis to the missing area was combined with coronary reshaping with resin composite and orthodontic therapy. The treatment began with the extraction of the deciduous upper left central incisor, bone preparation for the recipient site of the donor tooth, atraumatic extraction of the right upper second premolar and immediate autotransplantation in the surgically prepared recipient site. Subsequently, sutures to reposition the flap and a rigid splint were performed. After 12 months, coronary reshaping of the autotransplanted tooth with resin composite was carried out. Orthodontic treatment involving the use of a fixed appliance was used to correct the interdental spaces and achieve adequate occlusion. Clinical and radiographic follow-up 10 years after tooth autotransplantation and 9 years after reshaping revealed partial obliteration of the pulp chamber, root resorption, ankylosis and the presence of endodontic treatment. The long-term outcomes highlighted that tooth autotransplantation represents a biologically and cost-effective procedure for replacing missing teeth in young patients, particularly in cases of incomplete rhizogenesis of the autotransplanted tooth. This case report discusses tooth autotransplantation and resin composite reshaping as viable and long-term clinical options for treating young patients with dental agenesis.

An effective video inpainting technique using morphological Haar wavelet transform with krill herd based criminisi algorithm

In recent times, video inpainting techniques have intended to fill the missing areas or gaps in a video by utilizing known pixels. The variety in brightness or difference of the patches causes the state-of-the-art video inpainting techniques to exhibit high computation complexity and create seams in the target areas. To resolve these issues, this paper introduces a novel video inpainting technique that employs the Morphological Haar Wavelet Transform combined with the Krill Herd based Criminisi algorithm (MHWT-KHCA) to address the challenges of high computational demand and visible seam artifacts in current inpainting practices. The proposed MHWT-KHCA algorithm strategically reduces computation times and enhances the seamlessness of the inpainting process in videos. Through a series of experiments, the technique is validated against standard metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), where it demonstrates superior performance compared to existing methods. Additionally, the paper outlines potential real-world applications ranging from video restoration to real-time surveillance enhancement, highlighting the technique’s versatility and effectiveness. Future research directions include optimizing the algorithm for diverse video formats and integrating machine learning models to advance its capabilities further.

Poster 138: Best-fit Circle Missing Area Method Shows Good Accuracy and Inter-rater Reliability When Assessing Glenoid Bone Loss

Poster 138: Best-fit Circle Missing Area Method Shows Good Accuracy and Inter-rater Reliability When Assessing Glenoid Bone Loss

Study on Dynamic Scanning Trajectory of Large Aerospace Parts Based on 3D Scanning

The aim of manufacturing large aerospace parts for the three-dimensional scanning field demands high precision and efficiency. However, it may be more challenging to meet the full coverage of the measurement problems for large aerospace parts with the scanning range of traditional three-dimensional scanning methods. This paper establishes a dynamic posturing scanning measurement system for large aerospace parts with a six-degree-of-freedom posturing platform and a six-degree-of-freedom industrial robot linkage. It establishes a mathematical model of dynamic three-dimensional scanning posturing. It proposes a platform attitude adjustment strategy based on the field of view angle of a 3D scanner during the adjustment of a six-degree-of-freedom platform. The dynamic scanning path planning is carried out using the three-dimensional spatial decomposition method, and the vector coordinates of the critical points at the edges of the missing areas of the scan are used to re-scan the missing areas to establish the dynamic scanning paths of large aerospace parts. It is experimentally verified that the system can realize the dynamic scanning of complex curved large aerospace parts. The experimental results show that the measurement efficiency is improved by more than 75%, and the point cloud coverage of the scanning reconstruction is improved by 18% for large aerospace components with complex surfaces.

Progressive gap-filling in optical remote sensing imagery through a cascade of temporal and spatial reconstruction models

Filling gaps caused by thick cloud cover or sensor malfunctions has always posed a significant challenge in the preprocessing of optical remote sensing images. The concept of similar pixels, derived from spatial and temporal similarities in remote sensing scenes, has been widely embraced and extensively applied, leading to the development of various gap-filling models. However, in complex scenarios characterized by spatiotemporally heterogeneous surfaces and extensive missing areas, current models often produce noticeable noise-like artifacts, distorted spectral signatures, and unreliable spatial textures. To address this challenge, we propose a simple yet effective similar-pixel-based approach called progressive gap-filling through the cascading temporal and spatial framework (PGFCTS). Drawing inspiration from the negative correlation between spatial distances and the robustness of similar pixels, we employ a progressive gap-filling scheme to ensure that similar pixels are spatially close to the target pixel. This significantly enhances the effectiveness of similar pixels and improves the accuracy of the reconstruction model. Moreover, unlike traditional methods that integrate spatial and temporal information in parallel, our approach integrates these two sources in a cascading manner. Initially, a temporal model is used to obtain preliminary results with fine texture details, followed by a spatial model to enhance spectral fidelity. Through tests on two gap-filling missions and comparisons with seven classical methods, results demonstrate that PGFCTS effectively eliminates noise-like artifacts, faithfully restores spectral features, and accurately reproduces spatial details. Quantitative assessment reveals that PGFCTS consistently outperforms other methods, securing the best scores. Importantly, our method maintains its superiority over extended time intervals between the target and reference images. In summary, PGFCTS emerges as an effective solution for filling missing gaps and reproducing surface information, thereby enhancing the usability of optical images.

Unsupervised Domain Adaptation Depth Estimation Based on Self-attention Mechanism and Edge Consistency Constraints

In the unsupervised domain adaptation (UDA) (Akada et al. Self-supervised learning of domain invariant features for depth estimation, in: 2022 IEEE/CVF winter conference on applications of computer vision (WACV), pp 3377–3387 (2022). 10.1109/WACV51458.2022.00107) depth estimation task, a new adaptive approach is to use the bidirectional transformation network to transfer the style between the target and source domain inputs, and then train the depth estimation network in their respective domains. However, the domain adaptation process and the style transfer may result in defects and biases, often leading to depth holes and instance edge depth missing in the target domain’s depth output. To address these issues, We propose a training network that has been improved in terms of model structure and supervision constraints. First, we introduce a edge-guided self-attention mechanism in the task network of each domain to enhance the network’s attention to high-frequency edge features, maintain clear boundaries and fill in missing areas of depth. Furthermore, we utilize an edge detection algorithm to extract edge features from the input of the target domain. Then we establish edge consistency constraints between inter-domain entities in order to narrow the gap between domains and make domain-to-domain transfers easier. Our experimental demonstrate that our proposed method effectively solve the aforementioned problem, resulting in a higher quality depth map and outperforming existing state-of-the-art methods.

Hierarchical Vector-Quantized Variational Autoencoder and Vector Credibility Mechanism for High-Quality Image Inpainting

Image inpainting infers the missing areas of a corrupted image according to the information of the undamaged part. Many existing image inpainting methods can generate plausible inpainted results from damaged images with the fast-developed deep-learning technology. However, they still suffer from over-smoothed textures or textural distortion in the cases of complex textural details or large damaged areas. To restore textures at a fine-grained level, we propose an image inpainting method based on a hierarchical VQ-VAE with a vector credibility mechanism. It first trains the hierarchical VQ-VAE with ground truth images to update two codebooks and to obtain two corresponding vector collections containing information on ground truth images. The two vector collections are fed to a decoder to generate the corresponding high-fidelity outputs. An encoder then is trained with the corresponding damaged image. It generates vector collections approximating the ground truth by the help of the prior knowledge provided by the codebooks. After that, the two vector collections pass through the decoder from the hierarchical VQ-VAE to produce the inpainted results. In addition, we apply a vector credibility mechanism to promote vector collections from damaged images and approximate vector collections from ground truth images. To further improve the inpainting result, we apply a refinement network, which uses residual blocks with different dilation rates to acquire both global information and local textural details. Extensive experiments conducted on several datasets demonstrate that our method outperforms the state-of-the-art ones.

Flexible pavement longitudinal joint quality evaluation using non-destructive testing

Longitudinal joint construction quality is critical to the life of flexible pavements. Maintaining deteriorated longitudinal joints has become a challenge for many highway agencies. Improving the joint's quality through better compaction during construction can help achieve flexible pavements with longer service lives and less maintenance. Current quality control (QC) and quality assurance (QA) plans provide limited coverage. Consequently, the risk of missing areas with poor joint compaction is significant. A density profiling system (DPS) is a non-destructive alternative to conventional destructive evaluation methods. It can provide quick and continuous real-time coverage of the compaction during construction in dielectrics. The paper presents several case studies comparing various types of longitudinal joints and demonstrating the use of DPS to evaluate the joint's compaction quality. The paper shows that dielectric measurements can provide valuable insight into the ability of various construction techniques to achieve adequate levels of compaction at the longitudinal joint. The paper proposes a dielectric-based longitudinal joint quality index (LJQI) to evaluate the relative compaction of the joint during construction. It also shows that adopting DPS for assessing the compaction of longitudinal joints can minimize the risk of agencies accepting poorly constructed joints, identify locations of poor quality during construction, and achieve better-performing flexible pavements.

Rehabilitation of missing teeth with severely tilted abutment tooth using telescopic bridge: A case report

Background: Prolonged missing teeth can cause the adjacent and or antagonist teeth to migrate towards the edentulous area which then complicate the rehabilitation process. From the patient perspective, it can also affect the functional and aesthetic aspect of their life. Hence, tooth loss whether in small or big number has to be replaced. Telescopic bridge defined as an artificial crown constructed to fit over a coping. It consists of inner and outer coping where the outer coping is cemented over the inner coping. Objective: To rehabilitate posterior missing teeth with severely tilted abutment tooth by fabrication of telescopic bridge. Case Report: A 55-year-old male patient came to prosthodontics specialty clinic at Universitas Airlangga Dental hospital on his volition with a chief complaint of replacement of missing one posterior tooth on both side of lower jaw as he has difficulty in chewing food and recurrence of food entrapment on the missing teeth area. The patient reported to have had missed his lower teeth since 5 years ago. The treatment chosen for this case was telescopic bridge fabrication. This treatment was conducted in order to correct the path of insertion of the prosthesis whilst give a satisfactory aesthetic and functional result. Conclusion: Telescopic bridge can restore aesthetic and functional aspect of missing teeth with a severely tilted abutment tooth.