Fine Details Research Articles

PLANES4LOD2: Reconstruction of LoD-2 building models using a depth attention-based fully convolutional neural network

Level of detail (LoD)-2 reconstruction is an inevitable task in digital twin-related applications such as disaster management, flood simulation, landslide simulation and solar panel recommendation. However, there is a lack of capable methods that can exploit fine details in RGB imagery and mitigate noise in photogrammetric digital surface models (DSMs). Our investigation is focused on the use of roof planes to achieve a geometrically complete and correct, and topologically consistent LoD-2 building reconstruction. Using UNet with the EfficientNet-B3 backbone, the developed approach starts with jointly predicting building sections and roof planes from the orthorectified RGB imagery and a photogrammetric DSM. The detected sections and planes are then vectorized by employing tree search and simplified with the Douglas Peucker algorithm. Subsequently, height values from the noisy input DSM and the vectorized image-based (and simplified) roof planes are used to derive 3D-planes. Finally, the building model is formed by computing plane intersections as the ridge lines. This study demonstrates that a well-designed depth attention module (DAM), which is the bottleneck of the UNet, can achieve a very good use of both spectral and depth features. The resultant 1-to-n correspondence between building section and roof plane benefits accurate and consistent building model reconstruction. Furthermore, it leads to a superior generalization capability of the proposed method. Experiments with 1437 buildings from the cities Cologne and Braunschweig, Germany, demonstrate the success of the proposed workflow in reconstructing compound buildings with complex roof structures. The achieved geometric mean absolute error (MAE) is 1.06m and 0.24m respectively. Comprehensive comparative evaluations showcase the superiority of the approach in terms of geometric completeness and accuracy, and topological consistence with. The improvement over SAT2LOD2 (Gui and Qin, 2021) is 1.12m in Cologne (data accessible at https://github.com/dlrPHS/GPUB) and 0.47m in Braunschweig in geometrical MAE.

A flow-based multi-scale learning network for single image stochastic super-resolution

Single image super-resolution (SISR) is still an important while challenging task. Existing methods usually ignore the diversity of generated Super-Resolution (SR) images. The fine details of the corresponding high-resolution (HR) images cannot be confidently recovered due to the degradation of detail in low-resolution (LR) images. To address the above issue, this paper presents a flow-based multi-scale learning network (FMLnet) to explore the diverse mapping spaces for SR. First, we propose a multi-scale learning block (MLB) to extract the underlying features of the LR image. Second, the introduced pixel-wise multi-head attention allows our model to map multiple representation subspaces simultaneously. Third, by employing a normalizing flow module for a given LR input, our approach generates various stochastic SR outputs with high visual quality. The trade-off between fidelity and perceptual quality can be controlled. Finally, the experimental results on five datasets demonstrate that the proposed network outperforms the existing methods in terms of diversity, and achieves competitive PSNR/SSIM results. Code is available at https://github.com/qianyuwu/FMLnet.

Pricean ignorance

ABSTRACT Richard Price’s moral epistemology provides a distinctive account, not only of the sources of our moral knowledge, but also of its limits – that is, of the moral truths that we do not and even cannot know. According to this moral epistemology, the fundamental moral truths are necessary rather than contingent; if they are knowable at all, they are knowable a priori. In general, fundamental moral truths are akin to mathematical truths. Specifically, these necessary moral truths are grounded in the essences of act-types, which are knowable through ‘intuition’ if knowable at all. However, Price firmly rejects utilitarianism, recognizing several different ‘branches of virtue’, which can conflict with each other. As he argues, we cannot know the truth about act-types that exemplify such conflicting branches of virtue if the competing considerations are too finely balanced. This view of moral epistemology is compared with those of several other philosophers, including W. D. Ross. It is argued that Price has a more unified view than Ross: for Price, all moral knowledge flows from a priori intuition, but this is compatible with our being ignorant of many of the fine details of the fundamental truths of morality.

Image Inpainting based on Dilated Neighborhood Attention

In response to the phenomenon that existing image restoration algorithms have blurry edges, incoherent textures, and lack clarity and delicacy for large-area missing images, a two-stage generative adversarial image restoration algorithm based on dilated neighborhood attention is proposed. This algorithm decouples image restoration into edge structure restoration and texture structure restoration, introduces a dilated neighborhood attention mechanism, and enhances the generator's focus on important information and structures in the image by constructing a residual attention network, thereby improving the perception and utilization of texture details, resulting in more realistic image views and finer texture details. This paper introduces the Binary Cross-Entropy with Logits loss function in the discriminators of the two stages, which can help the discriminator learn how to more effectively identify real and generated images, thus improving the overall network performance. The Ranger21 optimizer is introduced to accelerate learning without affecting generalization, addressing the problem of traditional optimizers systematically staying in poor initial states. The datasets used in this paper are Paris Street View and CelebA-HQ. Comparative experiments with other image restoration methods show that both peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) have improved, and the larger the mask area, the more significant the improvement. Experiments prove that the images restored by the proposed algorithm have more reasonable structures and richer details, and the image restoration effect is superior.

Structured residual sparsity for video compressive sensing reconstruction

Recent advancements in the residual sparsity strategy have garnered widespread attention in video compressive sensing (CS) reconstruction. However, most of the existing residual sparsity-based video CS reconstruction methods usually suffer from some limitations that lead to undesired visual artifacts. Firstly, these methods only rely on a patch sparsity scheme that is limited by their focus on the local structures of each video frame, neglecting the nonlocal self-similarity (NSS) property inherent to each video frame. Secondly, these methods concentrate on utilizing the NSS property of external reference frames for multi-hypothesis (MH) prediction while disregarding the internal NSS property of the current frame. In this paper, we propose a new structured residual sparsity (SRS) approach for video CS reconstruction, which jointly exploits the NSS properties of the current frame and its reference frames. Specifically, due to the unavailability of the original video frames, we first devise an effective intraframe CS (EICS) reconstruction method that leverages the internal NSS property of each frame. This approach enables us to obtain initial recovery frames, which then facilitate the execution of MH prediction. Following this, we generate a residual frame for the current frame by employing the MH prediction. Then, we propose a novel SRS model jointly using the NSS properties of the current frame and its reference frames to explore both the correlations of intraframe and interframe for reconstructing the current frame. Furthermore, for the sake of optimization feasibility, we develop an effective alternating direction method of multipliers (ADMM) algorithm to address the objective. Our experimental findings reveal that the proposed SRS not only yields superior quantitative results, but also uncovers finer details and causes fewer visual artifacts compared to many popular or state-of-the-art video CS reconstruction approaches.

MDANet: A High-Resolution City Change Detection Network Based on Difference and Attention Mechanisms under Multi-Scale Feature Fusion

In the domains of geographic information systems and remote sensing image analysis, change detection is vital for examining surface variations in high-resolution remote sensing pictures. However, the intricate texture characteristics and rich details found in high-resolution remote sensing photos are difficult for conventional change detection systems to deal with. Target misdetection, missed detections, and edge blurring are further problems with current deep learning-based methods. This research proposes a high-resolution city change detection network based on difference and attention mechanisms under multi-scale feature fusion (MDANet) to address these issues and improve the accuracy of change detection. First, to extract features from dual-temporal remote sensing pictures, we use the Siamese architecture as the encoder network. The Difference Feature Module (DFM) is employed to learn the difference information between the dual-temporal remote sensing images. Second, the extracted difference features are optimized with the Attention Refinement Module (ARM). The Cross-Scale Fusion Module (CSFM) combines and enhances the optimized attention features, effectively capturing subtle differences in remote sensing images and learning the finer details of change targets. Finally, thorough tests on the BTCDD dataset, LEVIR-CD dataset, and CDD dataset show that the MDANet algorithm performs at a cutting-edge level.

Projection domain decomposition denoising algorithm based on low rank and similarity-based regularization.

Projection Domain Decomposition (PDD) is a dual energy reconstruction method which implements the decomposition process before image reconstruction. The advantage of PDD is that it can alleviate beam hardening artifacts and metal artifacts effectively as energy spectra estimation is considered in PDD. However, noise amplification occurs during the decomposition process, which significantly impacts the accuracy of effective atomic number and electron density. Therefore, effective noise reduction techniques are required in PDD. This study aims to develop a new algorithm capable of minimizing noise while simultaneously preserving edges and fine details. In this study, a denoising algorithm based on low rank and similarity-based regularization (LRSBR) is presented. This algorithm incorporates the low-rank characteristic of tensors into similarity-based regularization (SBR) framework. This method effectively addresses the issue of instability in edge pixels within the SBR algorithm and enhances the structural consistency of dual-energy images. A series of simulation and practical experiments were conducted on a dual-layer dual-energy CT system. Experiments demonstrate that the proposed method outperforms exiting noise removal methods in Peak Signal-to-noise Ratio (PSNR), Root Mean Square Error (RMSE), and Structural Similarity (SSIM). Meanwhile, there has been a notable enhancement in the visual quality of CT images. The proposed algorithm has a significantly improved noise reduction compared to other competing approach in dual-energy CT. Meanwhile, the LRSBR method exhibits outstanding performance in preserving edges and fine structures, making it practical for PDD applications.

Adaptive weighted total variation expansion and Gaussian curvature guided low-dose CT image denoising network

The denoising task of low-dose CT images is a highly complex and uncertain inverse problem. Previous studies have primarily relied on convolutional neural network to reduce noise by learning the mapping from LDCT images to normal dose CT images. However, simply increasing the network depth alone is not an optimal choice due to the limited performance improvement and significant computational cost. In contrast, integrating prior knowledge of images with a model to assist in image reconstruction is a more efficient approach. This study proposes a new framework for denoising LDCT images, named Noise-Optimized Edge Feature Guided Network (NEFGN). The task of NEFGN is to integrate the noise optimization model of adaptive weighted total variation expansion, the edge detection model guided by Gaussian curvature, and image reconstruction into an end-to-end CNN framework. In order to achieve this goal, the noise optimization model is first constructed by learning the parameters in the adaptive weighted total variation regularization model to approximate the noise level in the NDCT image. The edge detection network is constructed using Gaussian curvature, predicting clear edges directly from the noise image. Finally, under the guidance of the noise optimization model and the edge detail model, NEFGN is more capable of suppressing artifact noise, demonstrating good accuracy and robustness, and can restore finer details. Numerous experimental studies demonstrate that the NEFGN denoising framework effectively restores the structure of LDCT images with limited image details and outperforms other methods in terms of performance.

Dual-space high-frequency learning for transformer-based MRI super-resolution

Background and ObjectiveMagnetic resonance imaging (MRI) can provide rich and detailed high-contrast information of soft tissues, while the scanning of MRI is time-consuming. To accelerate MR imaging, a variety of Transformer-based single image super-resolution methods are proposed in recent years, achieving promising results thanks to their superior capability of capturing long-range dependencies. Nevertheless, most existing works prioritize the design of transformer attention blocks to capture global information. The local high-frequency details, which are pivotal to faithful MRI restoration, are unfortunately neglected. MethodsIn this work, we propose a high-frequency enhanced learning scheme to effectively improve the awareness of high frequency information in current Transformer-based MRI single image super-resolution methods. Specifically, we present two entirely plug-and-play modules designed to equip Transformer-based networks with the ability to recover high-frequency details from dual spaces: 1) in the feature space, we design a high-frequency block (Hi-Fe block) paralleled with Transformer-based attention layers to extract rich high-frequency features; while 2) in the image intensity space, we tailor a high-frequency amplification module (HFA) to further refine the high-frequency details. By fully exploiting the merits of the two modules, our framework can recover abundant and diverse high-frequency information, rendering faithful MRI super-resolved results with fine details. ResultsWe integrated our modules with six Transformer-based models and conducted experiments across three datasets. The results indicate that our plug-and-play modules can enhance the super-resolution performance of all foundational models to varying degrees, surpassing the capabilities of existing state-of-the-art single image super-resolution networks. ConclusionComprehensive comparison of super-resolution images and high-frequency maps from various methods, clearly demonstrating that our module possesses the capability to restore high-frequency information, showing huge potential in clinical practice for accelerated MRI reconstruction.

LNMVSNet: A Low-Noise Multi-View Stereo Depth Inference Method for 3D Reconstruction.

With the widespread adoption of modern RGB cameras, an abundance of RGB images is available everywhere. Therefore, multi-view stereo (MVS) 3D reconstruction has been extensively applied across various fields because of its cost-effectiveness and accessibility, which involves multi-view depth estimation and stereo matching algorithms. However, MVS tasks face noise challenges because of natural multiplicative noise and negative gain in algorithms, which reduce the quality and accuracy of the generated models and depth maps. Traditional MVS methods often struggle with noise, relying on assumptions that do not always hold true under real-world conditions, while deep learning-based MVS approaches tend to suffer from high noise sensitivity. To overcome these challenges, we introduce LNMVSNet, a deep learning network designed to enhance local feature attention and fuse features across different scales, aiming for low-noise, high-precision MVS 3D reconstruction. Through extensive evaluation of multiple benchmark datasets, LNMVSNet has demonstrated its superior performance, showcasing its ability to improve reconstruction accuracy and completeness, especially in the recovery of fine details and clear feature delineation. This advancement brings hope for the widespread application of MVS, ranging from precise industrial part inspection to the creation of immersive virtual environments.

A novel method of evaluating the non-invasive tear film break-up time and progression of corneal opacification in dogs using imaging video.

This study aimed to determine the correlation of the parameters that indicate the status of the ocular surface with the prognosis of corneal opacification. Fifty dogs (96 eyes) were examined using a grid-line illuminator (non-invasive tear film break-up time (NIBUT)). Thirty dogs (54 eyes) were included in the final analysis based on the criteria. The NIBUT and tear film break-up time (TFBUT) results of the eyes included in the study were divided into three groups: Group 1 (< 5 s), Group 2 (5 to <10 s), and Group 3 (≥ 10 s). The Schirmer's tear Test 1 (STT-1) results of the included patients were also divided into three groups: Group 1 (< 5 mm/min), Group 2 (5 to <10 mm/min), and Group 3 (≥ 10 mm/min). The corneal opacity grades are divided into four scores, ranging from 0 to 3. The corneal opacity grade score (COS) of 0 indicates a completely clear cornea or only a trace of opacity. COS of 1, 2, 3 indicate the presence of a prominent corneal opacity that does not interfere with the visualization of the fine iris details, the opacity obscures the visibility of the iris and lens details and severe obstruction of the intraocular structure visibility, respectively. The mean difference in COS during the follow-ups for each group of NIBUT were 0.61 ± 0.92 (n = 28), 0.10 ± 0.32 (n = 10), 0.19 ± 0.40 (n = 16). The NIBUT groups were significantly correlated with COS (p-value = 0.073) at a 10% level of significance. Post-hoc test at a 10% level of significance revealed significant correlations between Groups 1 and 2 (p-value = 0.041) and between Groups 1 and 3 (p-value 0.104). Although the TFBUT and STT-1 groups did not show any significant correlation with COS. Eyes with NIBUT of <5 s were found to have a significantly higher chance of increased COS compared with eyes with NIBUT of >5 s in the grid-line illumination plate NIBUT test. Among NIBUT, STT-1, and TFBUT, NIBUT was the only test that showed significant associations with the changes in COS.

Sparse learning model with embedded RIP conditions for turbulence super-resolution reconstruction

In practical engineering scenarios, constraints arising from sensor placement, quantity, and the limitations of current testing technologies often lead to turbulence data characterized by low resolution and irregular structures. Turbulence super-resolution reconstruction is crucial for extracting finer details from irregularly structured, low-resolution measurement data, thereby facilitating comprehensive flow field analyses. This article introduces a sparse learning model (Embedding Restricted Isometry Property Autoencoder (RIP-AE)) for achieving super-resolution reconstruction of flow fields by obtaining compressed representations and sparse transform domain information. In this model, we embed the Restricted Isometry Property (RIP) condition to ensure the effectiveness of compressed representations and sparse transform domains, thereby enhancing the super-resolution reconstruction accuracy of the model. To assess the performance of the RIP-AE model, we utilized data sets of flow around a cylinder at different Reynolds numbers generated by unsteady Reynolds-Averaged Navier–Stokes simulations. We sequentially validate the effectiveness of the RIP condition at different Reynolds numbers (ranging from 1,000 to 500,000) and compare the reconstruction results of the RIP-AE model with those of the downsampled skip-connection/multi-scale (DSC/MS) and MS-AE models under different sampling ratios. The results indicate that the RIP-AE model excels in terms of L2 relative error and demonstrates the capability to achieve high-precision flow field reconstruction even under high sampling ratios.

Cross-domain colorization of unpaired infrared images through contrastive learning guided by color feature selection attention

In challenging lighting conditions, infrared detectors have become vital tools for enhancing visual perception, overcoming the limitations of visible cameras. However, inherent imaging principles and manufacturing constraints confine infrared imaging systems to grayscale, significantly impacting their utility. In comparison to visible imagery, infrared images lack detailed semantic information, color representation, and suffer from reduced contrast. While existing infrared image colorization techniques have made significant progress in improving color quality, challenges such as erroneous semantic color prediction and blurred depiction of fine details persist. Acquiring paired color images corresponding to real-world infrared scenarios poses substantial difficulties, exacerbating challenges in cross-domain colorization of infrared images. To address these critical issues, this paper introduces an innovative approach utilizing contrastive learning for unsupervised cross-domain mapping between unpaired infrared and visible color images. Additionally, we introduce a color feature selection attention module guiding rational infrared image coloring. The proposed method employs the Residual Fusion Attention Network (RFANet) as a generator, enhancing the encoder’s ability to represent color and structural features. Furthermore, to ensure structural content consistency and enhance overall color style matching accuracy, we design a comprehensive joint global loss function integrating both detailed content and color style. Experimental evaluations on publicly available datasets demonstrate the superior performance of the proposed unsupervised cross-domain colorization method for infrared images compared to previous approaches.

3D Face Reconstruction with Feature Enhancement using Bi-FPN for Forensic Analysis

The representation of facial features in three-dimensional space plays a pivotal role in various applications such as facial recognition, virtual reality, and digital entertainment. However, achieving high-fidelity reconstructions from two-dimensional facial images remains a challenging task, particularly in preserving fine texture details. This research addresses this problem by proposing a novel approach that leverages a combination of advanced techniques, including Resnet, Flame model, Bi-FPN, and a differential render architecture. The primary objective of this study is to enhance texture details in reconstructed 3D facial images. The integration of Bi-FPN (Bi-directional Feature Pyramid Network) enhances feature extraction and fusion across multiple scales, facilitating the preservation of texture details across different regions of the face. The objective is to accurately represent facial features from 2D images in three-dimensional space. By combining these methods, the proposed framework achieves significant improvements in preserving fine texture details and overall facial structure. Experimental results demonstrate the effectiveness of the approach, suggesting its potential for various applications such as virtual try-on and facial animation.

A new global multiplexing structure of original features for road crack detection

ABSTRACT This paper proposes a novel approach, referred to as Global Multiplexing of Original Features (GMOF), to detect cracks in images. The proposed approach leverages the shallow layers of a network to extract crack-edge details, which are then fused with deep features in later stages to improve detection accuracy. Specifically, GMOF utilises the first stage of a backbone network to extract shallow edge information, which is then resized to match the resolution of the subsequent stages. The original feature is then concatenated with the deep feature map, followed by a one-dimensional convolution to ensure channel consistency between stages. The experimental results on image classification and segmentation tasks show that GMOF helps the network learn fine edge details of cracks, resulting in a maximum improvement of 6.06% (classification) and 15.83% (segmentation) in accuracy. This crack detection method is easy to integrate into existing deep learning frameworks.

A hyperspectral surgical microscope with super-resolution reconstruction for intraoperative image guidance.

Hyperspectral imaging (HSI) is an emerging imaging modality in medical applications, especially for intraoperative image guidance. A surgical microscope improves surgeons' visualization with fine details during surgery. The combination of HSI and surgical microscope can provide a powerful tool for surgical guidance. However, to acquire high-resolution hyperspectral images, the long integration time and large image file size can be a burden for intraoperative applications. Super-resolution reconstruction allows acquisition of low-resolution hyperspectral images and generates high-resolution HSI. In this work, we developed a hyperspectral surgical microscope and employed our unsupervised super-resolution neural network, which generated high-resolution hyperspectral images with fine textures and spectral characteristics of tissues. The proposed method can reduce the acquisition time and save storage space taken up by hyperspectral images without compromising image quality, which will facilitate the adaptation of hyperspectral imaging technology in intraoperative image guidance.

LAMA-WAVELET: IMAGE IMPAINTING WITH HIGH QUALITY OF FINE DETAILS AND OBJECT EDGES

Context. The problem of the image impainting in computer graphic and computer vision systems is considered. The subject of the research is deep learning convolutional neural networks for image inpainting.&#x0D; Objective. The objective of the research is to improve the image inpainting performance in computer vision and computer graphics systems by applying wavelet transform in the LaMa-Fourier network architecture.&#x0D; Method. The basic LaMa-Fourier network decomposes the image into global and local texture. Then it is proposed to improve the network block, processing the global context of the image, namely, the spectral transform block. To improve the block of spectral transform, instead of Fourier Unit Structure the Simple Wavelet Convolution Block elaborated by the authors is used. In this block, 3D wavelet transform of the image on two levels was initially performed using the Daubechies wavelet db4. The obtained coefficients of 3D wavelet transform are splitted so that each subband represents a separate feature of the image. Convolutional layer, batch normalization and ReLU activation function are sequentially applied to the results of splitting of coefficients on each level of wavelet transform. The obtained subbands of wavelet coefficients are concatenated and the inverse wavelet transform is applied to them, the result of which is the output of the block. Note that the wavelet coefficients at different levels were processed separately. This reduces the computational complexity of calculating the network outputs while preserving the influence of the context of each level on image inpainting. The obtained neural network is named LaMa-Wavelet. The FID, PSNR, SSIM indexes and visual analysis were used to estimate the quality of images inpainted with LaMa-Wavelet network.&#x0D; Results. The proposed LaMa-Wavelet network has been implemented in software and researched for solving the problem of image inpainting. The PSNR of images inpainted using the LaMa-Wavelet exceeds the results obtained using the LaMa-Fourier network for narrow and medium masks in average by 4.5%, for large masks in average by 6%. The LaMa-Wavelet applying can enhance SSIM by 2–4% depending on a mask size. But it takes 3 times longer to inpaint one image with LaMa-Wavelet than with LaMa-Fourier network. Analysis of specific images demonstrates that both networks show similar results of inpainting of a homogeneous background. On complex backgrounds with repeating elements the LaMa-Wavelet is often more effective in restoring textures.&#x0D; Conclusions. The obtained LaMa-Wavelet network allows to improve the image inpainting with large masks due to applying wavelet transform in the LaMa network architecture. Namely, the quality of reconstruction of image edges and fine details is increased.

An Experimental Study on EUV‐To‐Magnetogram Image Translation Using Conditional Generative Adversarial Networks

AbstractDeep generative models have recently become popular in heliophysics for their capacity to fill in gaps in solar observational data sets, thereby helping mitigating the data scarcity issue faced in space weather forecasting. A particular type of deep generative models, called conditional Generative Adversarial Networks (cGAN), has been used since a few years in the context of image‐to‐image (I2I) translation on solar observations. These algorithms have however hyperparameters whose values might influence the quality of the synthetic image. In this work, we use magnetograms produced by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) and EUV images from the Atmospheric Imaging Assembly (AIA) for the problem of generating Artificial Intelligence (AI) synthetic magnetograms from multiple SDO/AIA channels using cGAN, and more precisely the Pix2PixCC algorithm. We perform a systematic study of the most important hyperparameters to investigate which hyperparameter might generate magnetograms of highest quality with respect to the Structural Similarity Index. We propose a structured way to perform training with various hyperparameter values, and provide diagnostic and visualization tools of the generated versus targeted image. Our results shows that when using a larger number of filters in the convolution blocks of the cGAN, the fine details in the generated magnetogram are better reconstructed. Adding several input channels besides the 304 Å channel does not improve the quality of generated magnetogram, but the hyperparameters controlling the relative importance of the different loss functions in the optimization process have an influence on the quality of the results.

Numerical investigation of water entry of hydrophobic spheres

We perform numerical simulations to study the dynamics of the entry of hydrophobic spheres in a pool of water. To track the air-water interface during the translation of the sphere in the pool of water, we use the volume of fluid model. The continuum surface force method computes the surface tension force. To represent the hydrophobic surface properties, we use wall adhesion in terms of a static contact angle. We perform simulations with different diameters and impact speeds of the sphere. Our simulations capture the formation of different types of air cavities, pinch-offs of these cavities, and other finer details similar to the experiments performed at the same parameters. Finally, we compare the drag force among the different hydrophobic cases. We further perform simulations of hydrophilic spheres impacting the pool of water and compare the drag force with the analogous hydrophobic cases. We conclude that the spheres with hydrophobic surfaces encounter a lower drag than their hydrophilic counterparts. This lower drag of the hydrophobic spheres is attributed to the formation of the air cavity by the hydrophobic surfaces while translating through the pool of water, which reduces the area of the sphere in contact with water. In contrast, no such air cavity forms in the case of hydrophilic spheres.

Medical Image Segmentation for Brain Tumor Detection Using U-NET

Abstract: Brain tumors encompass a wide variety of growths that can occur in the cranial cavity. These tumors can be benign or malignant, and early identification is important for suitable treatment. MRI is a non-invasive imaging method that provides detailed anatomical information and is useful in diagnosing and monitoring brain tumors. However, identifying tumors from complex MRI data remains a difficult and challenging task. This project uses image segmentation techniques to identify and identify tumors on MRI scans. The technology is works on the principles of deep learning, an intelligence process that enables computers to learn and recognize patterns in large data sets. This learning process helps doctors make informed decisions by distinguishing between healthy brain tissue and tumor growth. The unique design of the U-Net architecture, characterized by a U-shaped pattern with compactness and detail, makes it possible to capture complex patterns and fine details in medical images. Using different data from many brain MRI scans, the U-Net algorithm was trained to depict tumor regions with pixel-level accuracy.