Upsampling

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

Super-resolution for sentinel-2 images

Abstract: . Obtaining Sentinel-2 imagery of higher spatial resolution than the native bands while ensuring that output imagery preserves the original radiometry has become a key issue since the deployment of Sentinel-2 satellites. Several studies have been carried out on the upsampling of 20 m and 60 m Sentinel-2 bands to 10 meters resolution taking advantage of 10 m bands. However, how to super-resolve 10 m bands to higher resolutions is still an open problem. Recently, deep learning-based techniques has become a de facto standard for single-image super-resolution. The problem is that neural network learning for super-resolution requires image pairs at both the original resolution (10 m in Sentinel-2) and the target resolution (e.g., 5 m or 2.5 m). Since there is no way to obtain higher resolution images for Sentinel-2, we propose to consider images from others sensors having the greatest similarity in terms of spectral bands, which will be appropriately pre-processed. These images, together with Sentinel-2 images, will form our training set. We carry out several experiments using state-of-the-art Convolutional Neural Networks for single-image super-resolution showing that this methodology is a first step toward greater spatial resolution of Sentinel-2 images.

Dynamic Upsampling of Smoke through Dictionary-based Learning

Abstract: Simulating turbulent smoke flows with fine details is computationally intensive. For iterative editing or simply faster generation, efficiently upsampling a low-resolution numerical simulation is an attractive alternative. We propose a novel learning approach to the dynamic upsampling of smoke flows based on a training set of flows at coarse and fine resolutions. Our multiscale neural network turns an input coarse animation into a sparse linear combination of small velocity patches present in a precomputed over-complete dictionary. These sparse coefficients are then used to generate a high-resolution smoke animation sequence by blending the fine counterparts of the coarse patches. Our network is initially trained from a sequence of example simulations to both construct the dictionary of corresponding coarse and fine patches and allow for the fast evaluation of a sparse patch encoding of any coarse input. The resulting network provides an accurate upsampling when the coarse input simulation is well approximated by patches present in the training set (e.g., for re-simulation), or simply visually plausible upsampling when input and training sets differ significantly. We show a variety of examples to ascertain the strengths and limitations of our approach and offer comparisons to existing approaches to demonstrate its quality and effectiveness.

Upsampling and downsampling of motion maps and other auxiliary maps in a tiered signal quality hierarchy

Abstract: Certain configurations herein include changing the resolution of an auxiliary map (e.g., a motion map, a z-map, etc.) at a first level of quality to obtain an auxiliary map at a second level of quality. For example, changing the resolution can include receiving a respective auxiliary map of one or more vectors at one or more lower levels of quality and progressively refining, via novel operations, the auxiliary map to higher or lower levels of quality in a hierarchy.

Adaptive time-domain interpolation for OFDM scattered pilot symbols

Abstract: A receiver for use in a pilot-aided OFDM system and a method of performing channel length estimation of a channel in a wireless communication system includes using transmitted and received wireless signals to estimate a channel carrier function vector at continuous and scattered pilot positions of consecutive OFDM symbols; performing time-domain interpolation by (i) upsampling the estimated the channel carrier function vectors at the scattered pilot positions by inserting zeros in between estimated scattered pilot positions, and (ii) filtering the upsampled vectors using a finite impulse response filter comprising a filter bank comprising a plurality of filters; mapping the channel carrier function vector to only one of the filters in the filter bank located in the finite impulse response filter, wherein the mapping causes noise reduction and enhanced channel estimation thereby increasing a maximum Doppler frequency in the channel.

System and method of image upsampling

Abstract: A method includes receiving an image having a first resolution and generating an upsampled image having a second resolution based on the image. A multi-dimensional data structure corresponding to a multi-dimensional image space is generated from the upsampled image. Each node of the data structure is determined based on a weighted sum of values of one or more pixels in the upsampled image. Each of the one or more pixels corresponds to a pixel in the received image and is located within a region of the image space having a vertex defined by the node. A filter modifies the values of the nodes and a second upsampled image is generated based on the modified values of the nodes. Each pixel of the second upsampled image not corresponding to a pixel in the received image is determined based on a weighted sum of the modified values of one or more nodes.