Upsampling

About: Upsampling is a research topic. Over the lifetime, 2426 publications have been published within this topic receiving 57613 citations.

Fine-Grained Urban Flow Inference

Abstract: The ubiquitous deployment of monitoring devices in urban flow monitoring systems induces a significant cost for maintenance and operation. A technique is required to reduce the number of deployed devices, while preventing the degeneration of data accuracy and granularity. In this paper, we present an approach for inferring the real-time and fine-grained crowd flows throughout a city based on coarse-grained observations. This task exhibits two challenges: the spatial correlations between coarse- and fine-grained urban flows, and the complexities of external impacts. To tackle these issues, we develop a model entitled UrbanFM which consists of two major parts: 1) an inference network to generate fine-grained flow distributions from coarse-grained inputs that uses a feature extraction module and a novel distributional upsampling module; 2) a general fusion subnet to further boost the performance by considering the influence of different external factors. This structure provides outstanding effectiveness and efficiency for small scale upsampling. However, the single-pass upsampling used by UrbanFM is insufficient at higher upscaling rates. Therefore, we further present UrbanPy, a cascading model for progressive inference of fine-grained urban flows by decomposing the original tasks into multiple subtasks. Compared to UrbanFM, such an enhanced structure demonstrates favorable performance for larger-scale inference tasks.

Angular Upsampling in Infant Diffusion MRI Using Neighborhood Matching in x-q Space

Abstract: Diffusion MRI requires sufficient coverage of the diffusion wavevector space, also known as the q-space, to adequately capture the pattern of water diffusion in various directions and scales. As a result, the acquisition time can be prohibitive for individuals who are unable to stay still in the scanner for an extensive period of time, such as infants. To address this problem, in this paper we harness non-local self-similar information in the x-q space of diffusion MRI data for q-space upsampling. Specifically, we first perform neighborhood matching to establish the relationships of signals in x-q space. The signal relationships are then used to regularize an ill-posed inverse problem related to the estimation of high angular resolution diffusion MRI data from its low-resolution counterpart. Our framework allows information from curved white matter structures to be used for effective regularization of the otherwise ill-posed problem. Extensive evaluations using synthetic and infant diffusion MRI data demonstrate the effectiveness of our method. Compared with the widely adopted interpolation methods using spherical radial basis functions and spherical harmonics, our method is able to produce high angular resolution diffusion MRI data with greater quality, both qualitatively and quantitatively.

A Convolutional Decoder for Point Clouds using Adaptive Instance Normalization

Abstract: Automatic synthesis of high quality 3D shapes is an ongoing and challenging area of research. While several data-driven methods have been proposed that make use of neural networks to generate 3D shapes, none of them reach the level of quality that deep learning synthesis approaches for images provide. In this work we present a method for a convolutional point cloud decoder/generator that makes use of recent advances in the domain of image synthesis. Namely, we use Adaptive Instance Normalization and offer an intuition on why it can improve training. Furthermore, we propose extensions to the minimization of the commonly used Chamfer distance for auto-encoding point clouds. In addition, we show that careful sampling is important both for the input geometry and in our point cloud generation process to improve results. The results are evaluated in an auto-encoding setup to offer both qualitative and quantitative analysis. The proposed decoder is validated by an extensive ablation study and is able to outperform current state of the art results in a number of experiments. We show the applicability of our method in the fields of point cloud upsampling, single view reconstruction, and shape synthesis.

A discrete tomography approach for superresolution micro-CT images: application to bone

Abstract: In micro-CT imaging, the effective spatial resolution of the reconstructed images is generally limited by X-ray dose restrictions, the detector configuration or the scanning geometry. In this paper, we show that, using prior information on the grey values of the scanned objects, the spatial resolution of the reconstructed images can dramatically be improved. The proposed method is based on an upsampling of the reconstruction grid, combined with the DART algorithm (discrete algebraic reconstruction technique [1]), in which the scanned object is assumed to be composed of homogeneous materials. Experiments were run on simulated data as well as real X-ray CT data of the rat trabecular bone. Results show that the proposed method generates reconstructions with significantly more detail compared to conventional reconstruction algorithms.

Inter-layer reference picture construction for spatial scalability with different aspect ratios

Abstract: A method of coding video data includes upsampling at least a portion of a reference layer picture to an upsampled picture having an upsampled picture size. The upsampled picture size has a horizontal upsampled picture size and a vertical upsampled picture size. At least one of the horizontal or vertical upsampled picture sizes may be different than a horizontal picture size or vertical picture size, respectively, of an enhancement layer picture. In addition, position information associated with the upsampled picture may be signaled. An inter-layer reference picture may be generated based on the upsampled picture and the position information.