Texture Restoration Research Articles

Ablation Study of Diffusion Model with Transformer Backbone for Low-count PET Denoising.

Diffusion models (DM) built from a hierarchy of denoising autoencoders have achieved remarkable progress in image generation, and are increasingly influential in the field of image restoration (IR) tasks. In the meantime, its backbone of autoencoders also evolved from UNet to vision transformer, e.g. Restormer. Therefore, it is important to disentangle the contribution of backbone networks and the additional generative learning scheme. Notably, DM shows varied performance across IR tasks, and the performance of recent advanced transformer-based DM on PET denoising is under-explored. In this study, we further raise an intuitive question, "{if we have a sufficiently powerful backbone, whether DM can be a general add-on generative learning scheme to further boost PET denoising}". Specifically, we investigate one of the best-in-class IR models, i.e., DiffIR, which is a latent DM based on the Restormer backbone. We provide a qualitative and quantitative comparison with UNet, SR3 (UNet+pixel DM), and Restormer, on the 25% low dose 18F-FDG whole-body PET denoising task, aiming to identify the best practices. We trained and tested on 93 and 12 subjects, and each subject has 644 slices. It appears that Restormer outperforms UNet in terms of PSNR and MSE. However, additional latent DM over Restormer does not contribute to better MSE, SSIM, or PSNR in our task, which is even inferior to the conventional UNet. In addition, SR3 with pixel space DM is not stable to synthesize satisfactory results. The results are consistent with the natural image super-resolution tasks, which also suffer from limited spatial information. A possible reason would be the denoising iteration at latent feature space cannot well support detailed structure and texture restoration. This issue is more crucial in the IR tasks taking inputs with limited details, e.g., SR and PET denoising.

Tunneling nanotube-driven complete regeneration of murine fetal skin

This study investigated the three-dimensional (3D) cellular interactions and tunneling nanotubes (TNTs) during fetal mouse skin regeneration on embryonic days 13 (E13) and 15 (E15). We aimed to understand spatial relationships among cell types involved in skin regeneration and assess the potential role of TNTs. Full-thickness skin incisions were performed in E13 and E15 embryos. Wound sites were collected, embedded in epoxy resin, processed for 3D reconstruction (1 μm thickness sections), and subjected to whole-mount immunostaining. We conducted in vitro co-culture experiments with fetal macrophages and fibroblasts to observe TNT formation. To assess the effect of TNTs on skin regeneration, an inhibiting agent (cytochalasin B) was administered to amniotic fluid. Results revealed that E13 epidermal keratinocytes interacted with dermal fibroblasts and macrophages, facilitating skin regrowth. TNT structures were observed at the E13-cell wound sites, among macrophages, and between macrophages and fibroblasts, confirmed through in vitro co-culture experiments. In vitro and utero cytochalasin B administration hindered those formation and inefficient skin texture regeneration at E13 wound sites. This emphasizes the necessity of 3D cellular interactions between epidermal and dermal cells during skin regeneration in mouse embryos at E13. The prevalence of TNT structures indicated their involvement in achieving complete skin texture restoration.

Research on image restoration of [formula omitted] porous foam ceramics media using Transformer architecture

Pore structure images of foam ceramics play a crucial role in the field of refractory materials science and engineering, facilitating the observation and analysis of the material’s pore structure and unique characteristics. However, traditional image restoration techniques have limitations in handling background noise, complex texture restoration, deformation, and dislocation correction, as well as automation and intelligence aspects in restoring material pore structure images. The latest Transformer architecture has shown significant potential in image processing, particularly in image restoration research tasks. Nevertheless, its application in restoring pore structure images of materials remains unexplored. This paper aims to address this gap by focusing on the image restoration of foam ceramic pore structure images using a Transformer-based algorithm for super-resolution (SR) processing. Experimental comparisons with state-of-the-art methods demonstrate a remarkable improvement in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM), underscoring the effectiveness of the proposed approach.

Recasting Regional Lighting for Shadow Removal

Removing shadows requires an understanding of both lighting conditions and object textures in a scene. Existing methods typically learn pixel-level color mappings between shadow and non-shadow images, in which the joint modeling of lighting and object textures is implicit and inadequate. We observe that in a shadow region, the degradation degree of object textures depends on the local illumination, while simply enhancing the local illumination cannot fully recover the attenuated textures. Based on this observation, we propose to condition the restoration of attenuated textures on the corrected local lighting in the shadow region. Specifically, We first design a shadow-aware decomposition network to estimate the illumination and reflectance layers of shadow regions explicitly. We then propose a novel bilateral correction network to recast the lighting of shadow regions in the illumination layer via a novel local lighting correction module, and to restore the textures conditioned on the corrected illumination layer via a novel illumination-guided texture restoration module. We further annotate pixel-wise shadow masks for the public SRD dataset, which originally contains only image pairs. Experiments on three benchmarks show that our method outperforms existing state-of-the-art shadow removal methods. Project page in: yuhaoliu7456.github.io/RRL-Net.

Learn to enhance the low-light image via a multi-exposure generation and fusion method

In low-light image enhancement, single-exposure images contain a limited dynamic range, which hinders the restoration of contrast and texture. To address these problems, we propose a multi-exposure generation and fusion method (MEGF), which simulates multi exposure images and performs feature fusion for low light image enhancement. First, we propose a Multi-Exposure Generation (MEG) block, which generates images with different exposure levels based on the input low-light images. The MEG block employs information entropy as an evaluation measure to prevent the underexposed or overexposed image generation. Then, the Perceptual Importance based Multi-Exposure Feature Enhancement (PIMEFE) module has been developed to fuse the multi-exposure features using the Perceptual Importance-based Feature Fusion (PIFF) module. The PIFF module selects the well-exposed features from the multi-exposure features processed by the Multi Scale Recursive Feature Enhancement (MSRFE) block. Finally, the fused features are input to the Curve Adjustment (CA) block for fine-tuning and provide color enhancement to the fused features. Moreover, we propose the Multiple Exposure Recursive Fusion (MERF) module which estimates the adjustment factors for the CA block with the guidance of multi-exposure features. Experimental results demonstrate that our method outperforms other techniques in terms of image signal-to-noise ratio, structural similarity, and color accuracy on both real and synthetic datasets.

Texture-aware and color-consistent learning for underwater image enhancement

Texture and color are pivotal factors for evaluating the quality of underwater image enhancement. However, current methods for enhancing underwater images still exhibit deficiencies in the restoration of texture and color. Diverging from previous approaches, we conduct heuristic modeling specifically targeting color and texture, culminating in the proposal of a texture-aware and color-consistent network (TACC-Net). Specifically, it first obtains preliminary enhanced images of more global features to facilitate feature decoupling. Then, we designed a color feature decoupling (CFD) sub-module to decouple the preliminary enhanced images into color-consistent color histograms to generate textured gray images. Finally, we designed a color space adjustment embedding (CSAE) sub-module, which is used to evaluate the similarity between features when embedding color information, and adjust the embedding of color information on textures. The experimental results indicate that our method enhances images and exhibits strong competitiveness in improving visual quality.

Generating high-quality texture via panoramic feature aggregation for large mask inpainting

With the development of image generation and processing techniques, image inpainting techniques based on deep learning have achieved impressive results. Especially the emphasis on global context during inpainting enables the network to generate reasonably coarse inpainting results at low resolutions. However, how to better achieve high-quality texture filling at high resolutions is still a challenging problem. To address this problem, most methods design two-stage networks to achieve structure and texture restoration separately. But in the face of large-scale masks, the generated textures still suffer from blurring and artifacts. Therefore, in order to achieve inpainting of images with large-scale masks and generate fine textures, this paper proposes an end-to-end generative adversarial model for large mask inpainting, called Panoramic Feature Aggregation Network (PFAN). First, this paper designs a Euclidean Attention Mechanism (EAM) which exploits encoder features to generate low-resolution structure restoration. Then a Feature Aggregation Synthesis Block (FASB) is proposed in the decoder to achieve high-resolution complex texture filling. With the global receptive fields of these two modules, texture filling results with satisfactory performance even under large-scale masks. Experiments on CelebA-HQ, Paris Street View and FFHQ datasets show that the proposed method has superior performance.

Advancing sun glint correction in high-resolution marine UAV RGB imagery for coral reef monitoring

Sun glint presents a significant challenge in marine ecological remote sensing by obscuring valuable features of benthic communities, thus hindering accurate monitoring of these communities. More specifically, sun glint leads to inaccurate coral reef identification when it comes to utilize the high-resolution marine Unmanned Aerial Vehicle (UAV) imagery. Despite the availability of many physical models that address this problem using ocean remote sensing satellite images, there is still a lack of effective sun glint correction solutions for high-resolution RGB imagery obtained by consumer-grade UAVs in marine ecological monitoring, particularly at the centimeter level. In this study, an innovative sun glint correction pipeline combining a Foreground Attention-based Semantic Segmentation Network (FANet) with optical flow-based pixel propagation has been proposed, aiming to achieve accurate sun glint detection and restoration of real texture of coral reefs. To further understand the inner mode of sun glint segmentation FANet model, we conduct explainable analysis to ensure that it learns the informative sun glint features and makes reliable decisions. According to the comprehensive experimental evaluation using three datasets, our FANet model exhibits accurate and effective sun glint detection, achieving an IoU range of 70.86%–83.96%. Moreover, the sun-glint free images processed by our sun glint correction method have been employed for large-scale image stitching and shallow-water coral reef habitat semantic mapping. The results demonstrate that our sun glint correction approach successfully restores the texture of coral reefs and prepares a reliable foundation for subsequent downstream applications. We believe that our method as an open-source sun glint correction solution in high-resolution marine UAV RGB images is able to enhance the accuracy and efficiency of various marine ecological remote sensing tasks at a low-cost. The relevant code can be found in https://github.com/jyqinnn/Sun-glint-correction.

Progressive network based on detail scaling and texture extraction: A more general framework for image deraining

Many feature extraction components have been proposed for image deraining tasks, aiming to improve feature learning. However, few models have addressed the integration of multi-scale features from derain images. The fusion of multiple features at different scales in one model has the potential to significantly enhance the authenticity and detail of rainy images restoration. This study introduces a migratable multi-scale feature blending model, which is a progressive learning model based on detail dilation and texture extraction. First, the degraded image is sent to the detail dilation module, which is designed to increase the detailed outline and obtain the coarse image features. Second, the extracted feature maps are sent to the multi-scale feature extraction (MFE) module and the multi-scale hybrid strategy (MHS) module for improved texture restoration. Third, the simple convolution modules are replaced by an optimized transformer model to more efficiently extract contextual features and multi-scale information in images. Finally, a progressive learning strategy is employed to incrementally restore the degraded images. Empirical results show that our proposed module for progressive restoration achieves near state-of-the-art performance in several rain removal tasks. In particular, our model exhibits better rain removal realism compared to state-of-the-art models. The source code is available at https://github.com/JackAILab/DTPNet.

Toward Interactive Self-Supervised Denoising

Self-supervised denoising frameworks have recently been proposed to learn denoising models without noisy-clean image pairs, showing great potential in various applications. The denoising model is expected to produce visually pleasant images without noise patterns. However, it is non-trivial to achieve this goal using self-supervised methods because 1) the self-supervised model is difficult to restore the perceptual information due to the lack of clean supervision, and 2) perceptual quality is relatively subjective to users’ preferences. In this paper, we make the first attempt to build an interactive self-supervised denoising model to tackle the aforementioned problems. Specifically, we propose an interactive two-branch network to effectively restore perceptual information. The network consists of a denoising branch and an interactive branch, where the former focuses on efficient denoising, and the latter modulates the denoising branch. Based on the delicate architecture design, our network can produce various denoising outputs, allowing the user to easily select the most appealing outcome for satisfying the perceptual requirement. Moreover, to optimize the network with only noisy images, we propose a novel two-stage training strategy in a self-supervised way. Once the network is optimized, it can be interactively changed between noise reduction and texture restoration, providing more denoising choices for users. Existing self-supervised denoising methods can be integrated into our method to be user-friendly with interaction. Extensive experiments and comprehensive analyses are conducted to validate the effectiveness of the proposed method.

ZITS++: Image Inpainting by Improving the Incremental Transformer on Structural Priors.

Image inpainting involves filling missing areas of a corrupted image. Despite impressive results have been achieved recently, restoring images with both vivid textures and reasonable structures remains a significant challenge. Previous methods have primarily addressed regular textures while disregarding holistic structures due to the limited receptive fields of Convolutional Neural Networks (CNNs). To this end, we study learning a Zero-initialized residual addition based Incremental Transformer on Structural priors (ZITS++), an improved model upon our conference work, ZITS (Dong et al. 2022). Specifically, given one corrupt image, we present the Transformer Structure Restorer (TSR) module to restore holistic structural priors at low image resolution, which are further upsampled by Simple Structure Upsampler (SSU) module to higher image resolution. To recover image texture details, we use the Fourier CNN Texture Restoration (FTR) module, which is strengthened by Fourier and large-kernel attention convolutions. Furthermore, to enhance the FTR, the upsampled structural priors from TSR are further processed by Structure Feature Encoder (SFE) and optimized with the Zero-initialized Residual Addition (ZeroRA) incrementally. Besides, a new masking positional encoding is proposed to encode the large irregular masks. Compared with ZITS, ZITS++ improves the FTR's stability and inpainting ability with several techniques. More importantly, we comprehensively explore the effects of various image priors for inpainting and investigate how to utilize them to address high-resolution image inpainting with extensive experiments. This investigation is orthogonal to most inpainting approaches and can thus significantly benefit the community.

SEA-Net: Structure-Enhanced Attention Network for Limited-Angle CBCT Reconstruction of Clinical Projection Data

Limited-angle CBCT (LA-CBCT) is of great clinical interest, because the scanning time and the patient dose are proportional to the scanning range of gantry rotation angles of CBCT. However, the image reconstruction for LA-CBCT remains technically challenging, which suffers from severe wedge artifacts and image distortions. This work aims to improve LA-CBCT by developing deep learning (DL) methods for real clinical CBCT projection data, which is the first feasibility study of clinical-projection-data-based LA-CBCT, to the best of our knowledge. Targeting at real clinical projection data, we have explored various DL methods such as image/data/hybrid-domain methods and finally developed a so-called Structure-Enhanced Attention Network (SEA-Net) method that has the best image quality from clinical projection data among the DL methods we have implemented. Specifically, the proposed SEA-Net employs a specialized structure enhancement sub-network to promote texture preservation. Based on the observation that the distribution of wedge artifacts in reconstruction images is non-uniform, the spatial attention module is utilized to emphasize the relevant regions while ignores the irrelevant ones, which leads to more accurate texture restoration. SEA-Net was validated in comparison with analytic (FDK), iterative (TV), image-domain DL (DDNet and FED-INet, data-domain DL (DCAR), dual-domain DL (Sam'Net), and various unrolling DL (hdNet, CTNet, FSR-Net, CasRedSCAN) methods. Among all methods, the SEA-Net had the best image reconstruction quality as quantified by root-mean-square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM), for various LA-CBCT problems of 90°-180° projection data. In addition, LA-CBCT via SEA-Net provided comparable accuracy for both patient setup (quantified by image registration accuracy from planning CT (pCT) to CBCT) and dose calculation (see the table), with full-view CBCT. We explored various DL methods and developed an image-domain-based method termed SEA-Net that provided the best image quality for clinical projection data. To the best of our knowledge, this is the first feasibility study of the real clinical-projection-data-based LA-CBCT. Moreover, LA-CBCT via SEA-Net can potentially provide comparable accuracy for patient setup and dose calculation, with full-view CBCT.

Illumination Removal via Gaussian Difference L0 Norm Model for Facial Experssion Recognition

Face images in the logarithmic space can be considered as a sum of the texture component and lighting map component according to Lambert Reflection. However, it is still not easy to separate these two parts, because face contour boundaries and lighting change boundaries are difficult to distinguish. In order to enhance the separation quality of these to parts, this paper proposes an illumination standardization algorithm based on extreme L0 Gaussian difference regularization constraints, assuming that illumination is massively spread all over the image but illumination change boundaries are simple, regular, and sparse enough. The proposed algorithm uses an iterative L0 Gaussian difference smoothing method, which achieves a more accurate lighting map estimation by reserving the fewest boundaries. Thus, the texture component of the original image can be restored better by simply subtracting the lighting map estimated. The experiments in this paper are organized with two steps: the first step is to observe the quality of the original texture restoration, and the second step is to test the effectiveness of our algorithm for complex face classification tasks. We choose the facial expression classification in this step. The first step experimental results show that our proposed algorithm can effectively recover face image details from extremely dark or light regions. In the second step experiment, we use a CNN classifier to test the emotion classification accuracy, making a comparison of the proposed illumination removal algorithm and the state-of-the-art illumination removal algorithm as face image preprocessing methods. The experimental results show that our algorithm works best for facial expression classification at about 5 to 7 percent accuracy higher than other algorithms. Therefore, our algorithm is proven to provide effective lighting processing technical support for the complex face classification problems which require a high degree of preservation of facial texture. The contribution of this paper is, first, that this paper proposes an enhanced TV model with an L0 boundary constraint for illumination estimation. Second, the boundary response is formulated with the Gaussian difference, which strongly responds to illumination boundaries. Third, this paper emphasizes the necessity of reserving details for preprocessing face images.

Multi-layer and Multi-scale feature aggregation for DIBR-Synthesized image quality assessment

Depth-Image-Based Rendering (DIBR) is one of the main fundamental techniques for generating new viewpoints in 3D video applications such as multi-viewpoint video (MVV), free viewpoint video (FVV) and virtual reality (VR). Due to the imperfections of color images, depth maps or texture restoration techniques, several types of distortions occur in synthesized views. However, most of related works evaluated the quality of DIBR-synthesized views by only detecting a specific type of distortion, such as stretching, black holes, blurring, etc., which were unable to accurately evaluate the quality of DIBR-synthesized views. In this paper, a new no-reference image quality assessment method is proposed to evaluate the quality of DIBR-synthesized images by combining multi-layer and multi-scale features of images. To be specific, the distortions introduced by different stages of virtual viewpoint synthesis are first analyzed, and then multi-layer and multi-scale features are extracted to estimate the degree of texture and structure distortions. As a result, individual quality scores associated with two types of distortions (e.g., structural distortion and texture distortion) are aggregated to an overall image quality. Experimental results on two publicly available DIBR datasets show that the method has better performance than the state-of-the-art models.Index Terms: image quality assessment, DIBR-synthesized image, distortion correction, BIQA.

RFA-Net: Residual feature attention network for fine-grained image inpainting

Although most existing methods using Generative Adversarial Networks (GAN) generally produce plausible results, there is a significant amount of artifacts and less-than-ideal restoration of textures when large regions are missing or the background of missing regions is complex. To address this issue, in this paper, we propose a novel texture-aware backbone net named RFA-Net for finer texture image inpainting. Compared to conventional encoder–decoder methods, our main contribution is proposing a novel RFA-Net adopt a non-pooling residual CNN structure with three novel modules, which retains texture features from shallow layers and adaptively learn the importance of certain channels and locations of features that may potentially benefit image inpainting. In addition, we propose a hybrid loss optimization (HLO) module to enable the generator to focus on the semantic and texture details of the inpainted contents. Experimental results demonstrate that our RFA-Net is able to recover texture details and ground-truth consistent images, and outperforms the state-of-the-art methods both in terms of image quality and quantitative metrics. Our source code and data are available online at https://github.com/Jamie-61/RFA-Net-Inpainting.

SEA-Net: Structure-Enhanced Attention Network for Limited-Angle CBCT Reconstruction of Clinical Projection Data.

This work aims to improve limited-angle (LA) cone beam computed tomography (CBCT) by developing deep learning (DL) methods for real clinical CBCT projection data, which is the first feasibility study of clinical-projection-data-based LA-CBCT, to the best of our knowledge. In radiation therapy (RT), CBCT is routinely used as the on-board imaging modality for patient setup. Compared to diagnostic CT, CBCT has a long acquisition time, e.g., 60 seconds for a full 360° rotation, which is subject to the motion artifact. Therefore, the LA-CBCT, if achievable, is of the great interest for the purpose of RT, for its proportionally reduced scanning time in addition to the radiation dose. However, LA-CBCT suffers from severe wedge artifacts and image distortions. Targeting at real clinical projection data, we have explored various DL methods such as image/data/hybrid-domain methods and finally developed a so-called Structure-Enhanced Attention Network (SEA-Net) method that has the best image quality from clinical projection data among the DL methods we have implemented. Specifically, the proposed SEA-Net employs a specialized structure enhancement sub-network to promote texture preservation. Based on the observation that the distribution of wedge artifacts in reconstruction images is non-uniform, the spatial attention module is utilized to emphasize the relevant regions while ignores the irrelevant ones, which leads to more accurate texture restoration.

Treatment of Basal Cell Carcinoma Involving the Medial Canthus by Multiple Local Flaps: A Case Report

Introduction: Basal cell carcinoma (BCC) is a skin malignancy that commonly appears on the face. Although surgical resection remains the therapeutic gold standard, it always produces a skin defect. Fine aesthetic reconstruction is a persisting challenge for all dermatologists. We herein present a case of BCC affecting the patient’s medial canthus. Case presentation: A 61-year-old woman presented with BCC of her right medial canthus. After lesion excision, a 2.5- × 3.5-cm skin defect remained in her canthal area. We used three local flaps to cover the defect according to aesthetic subunits. The defect over the nasal dorsum was covered by a glabellar flap, the upper eyelid defect was covered by a rotation advancement flap, and the lower eyelid defect was covered by a subcutaneous island pedicle flap. The result remained both functionally and aesthetically satisfactory throughout 2 months of follow-up. Discussion: Skin malignancies can occur in areas that require special treatment. The medial canthus is a fundamental element of the “facial triangle.” Restoration of color, texture, and thickness of the soft tissue is essential in this area. Full-thickness skin grafts and single flaps seem unsuitable for large defects in this region. Instead, a combination of local flaps according to aesthetic subunits can give desirable results. Conclusion: Defects in the medial canthus are difficult to reconstruct. This case provides a novel technique for the treatment of BCC in the medial canthus. Multiple local flaps were used to cover the defect with low tension, giving both a good cosmetic appearance and effective structural reconstruction.

A Novel Deep-Learning-Based Enhanced Texture Transformer Network for Reference Image Super-Resolution

The study explored a deep learning image super-resolution approach which is commonly used in face recognition, video perception and other fields. These generative adversarial networks usually have high-frequency texture details. The relevant textures of high-resolution images could be transferred as reference images to low-resolution images. The latest existing methods use transformer ideas to transfer related textures to low-resolution images, but there are still some problems with channel learning and detailed textures. Therefore, the study proposed an enhanced texture transformer network (ETTN) to improve the channel learning ability and details of the texture. It could learn the corresponding structural information of high-resolution texture images and convert it into low-resolution texture images. Through this, finding the feature map can change the exact feature of images and improve the learning ability between channels. We then used multi-scale feature integration (MSFI) to further enhance the effect of fusion and achieved different degrees of texture restoration. The experimental results show that the model has a good resolution enhancement effect on texture transformers. In different datasets, the peak signal to noise ratio (PSNR) and structural similarity (SSIM) were improved by 0.1–0.5 dB and 0.02, respectively.

BAM: a balanced attention mechanism to optimize single image super-resolution

Recovering texture information from the aliasing regions has always been a major challenge for single image super-resolution (SISR) task. These regions are often submerged in noise so that we have to restore texture details while suppressing noise. To address this issue, we propose an efficient Balanced Attention Mechanism (BAM), which consists of Avgpool Channel Attention Module (ACAM) and Maxpool Spatial Attention Module (MSAM) in parallel. ACAM is designed to suppress extreme noise in the large-scale feature maps, while MSAM preserves high-frequency texture details. Thanks to the parallel structure, these two modules not only conduct self-optimization, but also mutual optimization to obtain the balance of noise reduction and high-frequency texture restoration during the back propagation process, and the parallel structure makes the inference faster. To verify the effectiveness and robustness of BAM, we applied it to 10 state-of-the-art SISR networks. The results demonstrate that BAM can efficiently improve the networks' performance, and for those originally with attention mechanism, the substitution with BAM further reduces the amount of parameters and increases the inference speed. Information multi-distillation network (IMDN), a representative lightweight SISR network with attention, when the input image size is 200 × 200, the FPS of proposed IMDN-BAM precedes IMDN {8.1%, 8.7%, 8.8%} under the three SR magnifications of × 2, × 3, × 4, respectively. Densely residual Laplacian network (DRLN), a representative heavyweight SISR network with attention, when the scale is 60 × 60, the proposed DRLN-BAM is {11.0%, 8.8%, 10.1%} faster than DRLN under × 2, × 3, × 4. Moreover, we present a dataset with rich texture aliasing regions in real scenes, named realSR7. Experiments prove that BAM achieves better super-resolution results on the aliasing area.

Endo-Aesthetic rehabilitation of complicated crown-root fracture of maxillary lateral incisor by fragment reattachment: A case report

In case of traumatic injuries to the anterior teeth, fragment reattachment is considered a minimally invasive technique. Re-attaching the same tooth fragment, possesses the advantage, of biomimicry by complete restoration of form, function, texture, lustre, and translucency. Due to advancements in adhesive dentistry, simple re-attachment to complex fragment rehabilitation have become predictable and reliable. Various techniques have been proposed for fragment reattachment based on the extent of injury, type of injury, condition of the fragment, and time- lapse. This case report demonstrates the endo-aesthetic rehabilitation of a complicated crown root fracture using a fiber post on lateral incisor with fracture line on the cervical 1/3of the labial aspect, running obliquely and apically towards the palatal surface after 1 year follow-up.