Plane Image(平面图像)研究综述
Plane Image 平面图像 - Considering the natural isotropy of US imaging on biological tissue, for a certain patient, the mapping from low-resolution to high-resolution images can be established by learning the mapping from down-sampled in-plane images (low-resolution) to original in-plane US images (high-resolution), which enables the generation of high-resolution through-plane images. [1] Tunable lenses have been applied in microscopy for axial scanning to acquire multiplane images. [2] Using the frequency domain of an epipolar-plane image (EPI) to select the minimum and maximum depths of an object of interest (OOI) allows greater selectivity over traditional methods and the ability to re-focus a light field as the scene changes. [3] In digital breast tomosynthesis (DBT), in-plane images are of clinical utility, whereas images within transverse planes contain significant artifacts simply because the existing algorithms are not designed for reconstructing accurately images within transverse planes from extremely limited-angular-range data. [4] The image segmentation is based on K-means clustering applied on a five-plane image; the five planes being selected from seven planes with the use of the Karhunen-Loeve transform. [5] All-in-focus 3D perception image is synthesized from multi-plane images (MPIs) by utilizing the inter-image plane depths computed from the disparities caused across the boundaries and its smooth surface from image textures inside the respective boundaries of the 2D MCA image. [6] A new method for generating 3D images using both disparity parameters due to the color filtered aperture (CFA) and depth alignment of the multi-plane images for an acquired 2D image using a novel computational imaging system is presented. [7] In this study, it is the task to retrieve space and geometric data from planes as accurately as possible and the bulk of the information is extracted from a plane image by means of instance segments, such as the Cascade Mask R-CNN. [8] At 3 and 9 months postoperatively, 3-T MRI was performed using a dedicated knee coil, and the median SI of the intra-articular ACL graft was measured on sagittal-plane images. [9] This paper presents a computationally efficient framework in which a single focal-plane image is used to obtain a high-resolution reconstruction of dynamic aberrations. [10] Slant discrimination performance was measured as a function of the reference slant and the level of uncorrelated white noise added to the test-plane images in the left and right eye. [11] Recently, learning methods have been designed to create Multiplane Images (MPIs) for view synthesis. [12] We built our proposed model upon an existing technique that uses the layered representation called multiplane images (MPIs). [13] It is shown that, participating in these exhibitions as a collage artist and being an architect of the exposition, Sviatchenko used the techniques of plane image in the organization of the exhibition space. [14] Plane-by-plane images are obtained through numerical reconstruction [1, 2]; all z-plane information is encoded in a single hologram. [15] This paper studied the validation of the Kors’s technique by comparing the VCG plotted-by-plane images using two metrics of image processing. [16] Original method of reconstructing the real coordinates of moving objects from their plane images is presented. [17] Projection data were acquired using two acquisition modes, and in-plane images are reconstructed using Feldkamp-Davis-Kress (FDK) algorithm. [18] Based on single-plane images, angiogenesis was quantified (mean vascular area), in percentage, and tumor area (mm2) and perimeter (mm). [19] Then, the epipolar-plane images (EPI) were generated, and the depth image was reconstructed based on the specific linear structures emerging in EPI and the automatic depth estimation algorithm. [20] Instead, we propose an algorithm for view synthesis from an irregular grid of sampled views that first expands each sampled view into a local light field via a multiplane image (MPI) scene representation, then renders novel views by blending adjacent local light fields. [21] The registration of an entire 2D ultrasound contrast image sequence based on out-of-plane images is ineffective. [22] In these techniques, three-dimensional imaging incorporates multiplexed volume holographic gratings, which are formed in phenanthrenequinone poly(methyl methacrylate) (PQ-PMMA) photopolymer and act as spatial-spectral filters, to obtain multiplane images from a volumetric object without scanning. [23] We present a variety of new compositing techniques using Multi-plane Images (MPI's) [Zhou et al. [24] Students can also learn to recognize the stars in the sky on the plane images displayed that create a 3D space, thus expanding their knowledge of the sky. [25] An H2 hologram is a transplane image that is different from the well-known Denisyuk hologram in which the final image appears fully behind the surface of the glass plate. [26] We present a novel approach to view synthesis using multiplane images (MPIs). [27] Using both simulations and experiments, we show that the MIR algorithm outperforms the leading multiplane image-sharpening algorithm over a wide range of anisoplanatic conditions. [28] We are addressing the problem by proposing a masking algorithm based on light field epipolar-plane images (EPIs). [29] However, ST requires a precise estimation of the disparity range of the SSLF in order to design a shearlet system with decent scales and to pre-shear the sparsely-sampled Epipolar-Plane Images (EPIs) of the SSLF. [30] Next we learn to predict a multiplane images (MPIs) representation, which can then be used to synthesize a range of novel views of the scene, including views that extrapolate significantly beyond the input baseline, to allow for efficient view synthesis. [31]考虑到超声对生物组织成像的自然各向同性，对于某个患者，可以通过学习下采样的平面内图像（低分辨率）到原始图像的映射来建立从低分辨率到高分辨率图像的映射。 -平面美国图像（高分辨率），可以生成高分辨率的平面图像。 [1] 可调谐透镜已应用于显微镜中，用于轴向扫描以获取多平面图像。 [2] 使用核平面图像 (EPI) 的频域来选择感兴趣对象 (OOI) 的最小和最大深度，与传统方法相比具有更大的选择性，并且能够随着场景的变化重新聚焦光场。 [3] 在数字乳房断层合成 (DBT) 中，平面内图像具有临床实用性，而横向平面内的图像包含明显的伪影，这仅仅是因为现有算法并非设计用于从极其有限的角度范围数据中准确重建横向平面内的图像。 [4] 图像分割基于应用于五平面图像的 K-means 聚类；使用 Karhunen-Loeve 变换从七个平面中选择五个平面。 [5] 全焦点 3D 感知图像是从多平面图像 (MPI) 合成的，利用从 2D MCA 图像各自边界内的图像纹理中跨边界引起的差异计算的图像间平面深度及其平滑表面. [6] 提出了一种使用由于彩色过滤孔径 (CFA) 产生的视差参数和使用新型计算成像系统获取的 2D 图像的多平面图像的深度对齐来生成 3D 图像的新方法。 [7] 在这项研究中，任务是尽可能准确地从平面中检索空间和几何数据，并通过实例片段（例如 Cascade Mask R-CNN）从平面图像中提取大部分信息。 [8] 术后 3 个月和 9 个月，使用专用膝关节线圈进行 3-T MRI，并在矢状平面图像上测量关节内 ACL 移植物的中位 SI。 [9] 本文提出了一种计算有效的框架，其中使用单个焦平面图像来获得动态像差的高分辨率重建。 [10] 倾斜辨别性能被测量为参考倾斜和添加到左眼和右眼测试平面图像中的不相关白噪声水平的函数。 [11] 最近，已经设计了学习方法来创建用于视图合成的多平面图像 (MPI)。 [12] 我们在现有技术的基础上构建了我们提出的模型，该技术使用称为多平面图像 (MPI) 的分层表示。 [13] 可以看出，斯维亚琴科以拼贴艺术家的身份参加这些展览，并作为展览的建筑师，在展览空间的组织中使用了平面图像的技术。 [14] 通过数值重建获得逐平面图像[1, 2]；所有 z 平面信息都被编码在一个全息图中。 [15] 本文通过使用两种图像处理指标比较 VCG 绘制的平面图像，研究了 Kors 技术的验证。 [16] 提出了从运动物体的平面图像重建真实坐标的原始方法。 [17] 使用两种采集模式采集投影数据，并使用 Feldkamp-Davis-Kress (FDK) 算法重建平面内图像。 [18] 基于单平面图像，量化血管生成（平均血管面积）、百分比和肿瘤面积（mm2）和周长（mm）。 [19] 然后，生成核平面图像（EPI），并根据EPI中出现的特定线性结构和自动深度估计算法重建深度图像。 [20] 相反，我们提出了一种从不规则的采样视图网格中合成视图的算法，该算法首先通过多平面图像 (MPI) 场景表示将每个采样视图扩展为局部光场，然后通过混合相邻的局部光场来渲染新视图。 [21] 基于平面外图像的整个二维超声造影图像序列的配准是无效的。 [22] 在这些技术中，3D 成像结合了多路复用体全息光栅，这些光栅由菲醌聚甲基丙烯酸甲酯 (PQ-PMMA) 光聚合物形成并充当空间光谱过滤器，无需扫描即可从体积物体获得多平面图像。 [23] 我们提出了多种使用多平面图像 (MPI) 的新合成技术 [Zhou et al. [24] 学生还可以学习在创建 3D 空间的平面图像上识别天空中的星星，从而扩展他们对天空的认识。 [25] H2 全息图是一种跨平面图像，与著名的 Denisyuk 全息图不同，后者的最终图像完全出现在玻璃板表面的后面。 [26] 我们提出了一种使用多平面图像 (MPI) 进行视图合成的新方法。 [27] 通过模拟和实验，我们表明 MIR 算法在各种非等平面条件下优于领先的多平面图像锐化算法。 [28] 我们通过提出一种基于光场核平面图像 (EPI) 的掩蔽算法来解决这个问题。 [29] 然而，ST 需要精确估计 SSLF 的视差范围，以便设计具有适当尺度的剪切波系统并预剪切 SSLF 的稀疏采样核平面图像 (EPI)。 [30] 接下来，我们学习预测多平面图像 (MPI) 表示，然后可以将其用于合成一系列新的场景视图，包括显着超出输入基线外推的视图，以实现高效的视图合成。 [31]