Upsampling

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

A Holistically-Nested U-Net: Surgical Instrument Segmentation Based on Convolutional Neural Network

Abstract: Surgical instrument segmentation is an essential task in the domain of computer-assisted surgical system. It is critical to increase the context-awareness of surgeons during the operation. We propose a new model based on the U-Net architecture for surgical instrument segmentation, which aggregates multi-scale feature maps and has cascaded dilated convolution layers. The model adopts dense upsampling convolution instead of deconvolution for upsampling. We set the side loss function on each side-output layer. The loss function includes an output loss function and all side loss functions to supervise the training of each layer. To validate our model, we compare our proposed model with advanced architecture U-Net in the dataset consisting of laparoscopy images from multiple surgical operations. Experiment results demonstrate that our model achieves good performance.

Minimizing power consumption in direct sequence spread spectrum correlators by resampling IF samples-Part I: performance analysis

Abstract: Most modern digital receivers sample the received RF signal at an intermediate frequency (IF), then downconvert to the baseband in the digital domain. The baseband samples are subsequently correlated with a time-limited square pulse, which is readily implemented as an integrator. Since correlation is performed at the sampling frequency, the receiver correlates at an unnecessarily high frequency. This high frequency results in significant power dissipation especially if parallel correlators are employed. By downsampling after appropriate filtering to slightly above the signal Nyquist rate, correlation is achieved at a fraction of the sampling frequency, resulting in corresponding reduction in power consumption. Furthermore, by resampling to an integer number of samples per chip, architectural changes in the correlators are possible, allowing even more reduction in power consumption. In this paper, a resampling correlator with minimal hardware overhead that outperforms the conventional correlator is presented.

Three-dimensional visualization method based on point cloud and image data and system thereof

Abstract: The invention discloses a three-dimensional visualization method based on point cloud and image data and a system thereof. The method comprises the following steps of collecting image data and point cloud data of a target scene; carrying out upsampling on the point cloud data, projecting the point cloud data after the upsampling to the image data so as to carry out fusion, coloring the fused point cloud data and acquiring the colorful point cloud data; and using the colorful point cloud data to carry out three-dimensional rendering and acquiring a three-dimensional visualization model of the target scene. In the invention, point-cloud three-dimensional visualization from data acquisition and fusion to final rendering display is realized, which is good for development of a laser point cloud technology; and accessibility and usability of the point cloud data to a common user are increased.

Fusion of Hyperspectral and Panchromatic Images Using Generative Adversarial Network and Image Segmentation

Abstract: Hyperspectral (HS) image fusion aims at integrating a panchromatic (PAN) image and an HS image, featuring the fused image with the spatial quality of the former and the spectral diversity of the latter. The classic fusion algorithm generally includes three consecutive procedures that are upsampling, detail extraction, and detail injection. In this article, we propose an HS and PAN image fusion method based on generative adversarial network and local estimation of injection gain. Instead of upsampling the HS image by classical interpolation techniques, a generative adversarial super-resolution network (GASN) is designed to obtain the interpolated HS image in the fusion framework. GASN establishes a spectral-information-based discriminator to conduct adversarial learning with the generator, so as to preserve the spectral information of the low-resolution HS image. An image segmentation-based injection gain estimation (ISGE) algorithm is subsequently proposed for HS and PAN images fusion. The injection gain is estimated over image segments obtained by a binary partition tree approach to improve the fusion performance. The proposed GASN and ISGE are implemented into two credible global estimation pansharpening methods, and experimental results prove the performance improvement of the proposed method. The proposed method is also compared with existing state-of-the-art methods, and experiments on several public databases demonstrate that the proposed method is competitive or superior to the state-of-the-art fusion methods.