One method for interpolating the pixel values of several low-resolution (LR) images to create a single high-resolution (HR) image is to approximate the patches of a Delaunay triangulation with bivariate polynomials.
This course is organized around seven projects and a capstone assignment. Each project includes readings, quizzes, and discussions about concepts and tools in cartography and visualization. Throughout the course, students complete “mile marker” assignments that are designed to help them progress toward the capstone assignment. Through the course projects, students confront realistic problem scenarios that incorporate such skills and concepts as creating symbolization schemes, coordinate systems and map projections, creating isoline and other terrain representations, interpolation, classification schemes, multivariate representation and representation of data uncertainty. Those who successfully complete the course are able to design and produce effective reference and thematic maps using GIS software and can interpret and critique maps and related information graphics.
Triangulation is a tool that divides a surface into regions with certain common characteristics. The type of these characteristics depends on the type of triangulation. Delaunay triangulation can divide a surface into regions that are particularly well-suited for image processing applications.
The phase-locked loop (PLL) is a critical component in coherent communications receivers that is responsible for locking on to the carrier of a received modulated signal. A PLL adjusts the phase of a numerically-controlled oscillator to match that of the
Representation, analysis, and design of discrete time signals and systems. Review of Z-transforms, discrete-time Fourier transforms, and difference equations. Discrete-time processing of continuous-time signals. Decimation, interpolation, and sampling rate conversion. Flowgraph structures for DT systems. Time-and frequency-domain design techniques for recursive (IIR) and non-recursive (FIR) filters. Linear prediction. Discrete Fourier transform, FFT algorithm. Short-time Fourier analysis and filter banks. Multirate techniques. Hilbert transforms, Cepstral analysis, various applications.
Given that no consumer grade digital cameras can produce images with more than at most about 12-bits per color channel, and 8-bits per color channel is more common, to manipulate HDR images (of say 32-bits per color channel), one must find a way to "estimate" the 8-bits up to 32-bits. This creation of an HDR image can be accomplished using multiple images at different exposure levels (stops).
To perform geometric transforms on discrete images such as a rotation or zooming we need to first fit the discrete data to a continuous function. This can be done using splines. B-splines can be used to interpolate and form the continuous image from the discrete samples.
Discusses how polyphase filters can be used as intersample delay filters.
Examines ideas of ideal reconstruction of signals for a sampled, noisy original.
Introduction of interpolation and it's application.
This course analyzes the basic techniques for the efficient numerical solution of problems in science and engineering.
Basic introduction to splines. Link to other modules.
A sample-rate compressor removes (D-1) of every D input samples. A sample-rate expander inserts (U-1) zeros after every input sample.
Lagrange's interpolation formula is a simple and clever method for finding the unique polynomial of order L that exactly passes through L+1 distinct samples of a signal.
You will implement a multirate system that includes three finite impulse response filters. The sample-rate compression and expansion factors can be controlled in real time using a MATLAB graphical user interface.
You will implement a multirate system that includes three finite impulse response filters. The sample-rate compression and expansion factors can be controlled in real time using a MATLAB graphical user interface.
You will implement a multirate system that includes three fininte impulse response filters. The sample-rate compression and expansion factors can be controlled in real time using a MATLAB graphical user interface.
You will implement a multirate system that includes three finite impulse response filters.
You will implement a multirate system that includes three finite impulse response filters. The sample-rate compression and expansion factors can be controlled in real time using a MATLAB graphical user interface.
You will implement a multirate system that includes three finite impulse response filters. The sample-rate compression and expansion factors can be controlled in real time using a MATLAB graphical user interface.
