Upsampling

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

Intonation transformation for speech therapy and the like

Abstract: The intonation of speech is modified by an appropriate combination of resampling and time-domain harmonic scaling. Resampling increases (upsampling) or decreases (downsampling) the number of data points in a signal. Harmonic scaling adds or removes pitch cycles to or from a signal. The pitch of a speech signal can be increased by combining downsampling with harmonic scaling that adds an appropriate number of pitch cycles. Alternatively, pitch can be decreased by combining upsampling with harmonic scaling that removes an appropriate number of pitch cycles. The present invention can be implemented in an automated speech-therapy tool that is able to modify the intonation of prerecorded reference speech signals for playback to a user to emphasize the correct pronunciation by increasing the pitch of selected portions of words or phrases that the user had previously mispronounced.

GPU-Accelerated Time-of-Flight Super-Resolution for Image-Guided Surgery

Abstract: In the field of image-guided surgery, Time-of-Flight (ToF) sensors are of interest due to their fast acquisition of 3-D surfaces. However, the poor signal-to-noise ratio and low spatial resolution of today’s ToF sensors require preprocessing of the acquired range data. Superresolution is a technique for image restoration and resolution enhancement by utilizing information from successive raw frames of an image sequence. We propose a super-resolution framework using the graphics processing unit. Our framework enables interactive frame rates, computing an upsampled image from 10 noisy frames of 200 × 200 px with an upsampling factor of 2 in 109 ms. The root-mean-square error of the super-resolved surface with respect to ground truth data is improved by more than 20 % relative to a single raw frame.

Method for image processing using local statistics convolution

Abstract: A method for filtering a digital image, comprising segmenting the digital image into a plurality of tiles; computing tile histograms corresponding to each of the plurality of tiles; deriving a plurality of tile transfer functions from the tile histograms preferably using 1D convolutions; interpolating a tile transfer function from the plurality of tile transfer functions; and filtering the digital image with the interpolated tile transfer function. Many filters otherwise difficult to conceive or to implement are possible with this method, including an edge-preserving smoothing filter, HDR tone mapping, edge invariant gradient or entropy detection, image upsampling, and mapping coarse data to fine data.

Pseudohigh-Resolution Spectral Interrogation Scheme for Small Signals From FBG Sensors

Abstract: In this paper, we proposed a spectral interrogation scheme for the detection of small wavelength shift of fiber Bragg grating (FBG) accelerometer in a vibration test. Our findings indicate that the adopted upsampling procedure in the algorithm can effectively reconstruct small signals (wavelength shift < pixel resolution of diffraction grating-based interrogator) from low-resolution spectra that cannot be achieved by using other interrogation schemes. The reconstructed signals have good SNR and excellent coherence with the reference signal from a standard integrated electronic piezoelectric accelerometer in both time and frequency domains, as if they are extracted from the high-resolution spectra. The proposed method has opened a new possibility of employing low-cost FBG interrogation for accurate measurement and dynamic modal analysis in various industrial applications.

Downsampling of Bounded Bandlimited Signals and the Bandlimited Interpolation: Analytic Properties and Computability

Abstract: Downsampling and the computation of the bandlimited interpolation of discrete-time signals are two important concepts in signal processing. In this paper we analyze the downsampling operation regarding its impact on the existence and computability of the bounded bandlimited interpolation. We assume that the discrete-time signal is obtained by downsampling the samples of a bounded bandlimited signal that vanishes at infinity, and we study two problems. First, we investigate the existence of the bounded bandlimited interpolation for such discrete-time signals from a signal theoretic perspective and show that there exist signals for which the bounded bandlimited interpolation does not exist. Second, we analyze the algorithmic generation of the bounded bandlimited interpolation, using the concept of Turing computability. Turing computability models what is theoretically implementable on a digital computer. Interestingly, it turns out that even if the bounded bandlimited interpolation exists analytically, it is not always computable, which implies that there exists no algorithm on a digital computer that can always compute it. Computability is important in order that the approximation error be controlled. If a signal is not computable, we cannot ascertain whether the computed signal is sufficiently close to the true signal, i.e., we cannot verify every approximation accuracy.