Upsampling

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

Enhanced resolution in SAR/ISAR imaging using iterative sidelobe apodization

Abstract: Resolution enhancement techniques in radar imaging have attracted considerable interest in recent years. In this work, we develop an iterative sidelobe apodization technique and investigate its applications to synthetic aperture radar (SAR) and inverse SAR (ISAR) image processing. A modified noninteger Nyquist spatially variant apodization (SVA) formulation is proposed, which is applicable to direct iterative image sidelobe apodization without using computationally intensive upsampling interpolation. A refined iterative sidelobe apodization procedure is then developed for image-resolution enhancement. Examples using this technique demonstrate enhanced image resolution in various applications, including airborne SAR imaging, image processing for three-dimensional interferometric ISAR imaging, and foliage-penetration ultrawideband SAR image processing.

Multispectral demosaicking using adaptive kernel upsampling

Abstract: Multispectral demosaicking, which estimates full multispectral images from raw data observed using a single image sensor with a color filter array (CFA), is a challenging task because each spectral component is severely undersampled. In this paper, we propose a novel multispectral demosaicking algorithm. We extend existing upsampling algorithms to adaptive kernel upsampling algorithms using an adaptive kernel as a spatial weight and apply them to multispectral demosaicking. We also propose a new CFA and a direct adaptive kernel estimation from the raw data of the proposed CFA. Experimental results with real multispectral images demonstrate the effectiveness of the proposed algorithm.

Turning overlap-save into a multiband mixing, downsampling filter bank

Abstract: This paper demonstrates how the popular overlap-save (OS) fast convolution filtering technique can be extended to create a flexible and computationally efficient bank of filters, with frequency translation and decimation implemented in the frequency domain. The paper also provides some tips for choosing appropriate fast Fourier transform (FFT) size. It also presents implementation guidance to streamline this powerful, multichannel filtering, down-conversion and decimation process

Deep EEG super-resolution: Upsampling EEG spatial resolution with Generative Adversarial Networks

Abstract: Electroencephalography (EEG) activity contains a wealth of information about what is happening within the human brain. Recording more of this data has the potential to unlock endless future applications. However, the cost of EEG hardware is increasingly expensive based upon the number of EEG channels being recorded simultaneously. We combat this problem in this paper by proposing a novel deep EEG super­resolution (SR) approach based on Generative Adversarial Networks (GANs). This approach can produce high spatial resolution EEG data from low resolution samples, by generating channel-wise upsampled data to effectively interpolate numerous missing channels, thus reducing the need for expensive EEG equipment. We tested the performance using an EEG dataset from a mental imagery task. Our proposed GAN model provided ∼104 fold and ∼102 fold reduction in mean-squared error (MSE) and mean-absolute error (MAE), respectively, over the baseline bicubic interpolation method. We further validate our method by training a classifier on the original classification task, which displayed minimal loss in accuracy while using the super-resolved data. The proposed SR EEG by GAN is a promising approach to improve the spatial resolution of low density EEG headset.

Perceptually‐motivated Real‐time Temporal Upsampling of 3D Content for High‐refresh‐rate Displays

Abstract: High-refresh-rate displays (e. g., 120 Hz) have recently become available on the consumer market and quickly gain on popularity. One of their aims is to reduce the perceived blur created by moving objects that are tracked by the human eye. However, an improvement is only achieved if the video stream is produced at the same high refresh rate (i. e. 120 Hz). Some devices, such as LCD TVs, solve this problem by converting low-refresh-rate content (i. e. 50 Hz PAL) into a higher temporal resolution (i. e. 200 Hz) based on two-dimensional optical flow. In our approach, we will show how rendered three-dimensional images produced by recent graphics hardware can be up-sampled more efficiently resulting in higher quality at the same time. Our algorithm relies on several perceptual findings and preserves the naturalness of the original sequence. A psychophysical study validates our approach and illustrates that temporally up-sampled video streams are preferred over the standard low-rate input by the majority of users. We show that our solution improves task performance on high-refresh-rate displays.