42-731 Bioimage Informatics
12 units, Carnegie Mellon University
The goals of this course are to provide students with the following the ability to use mathematical techniques such as linear algebra. Fourier theory and sampling in more advanced signal processing settings fundamentals of multiresolution and wavelet techniques and indepth coverage of some bioimaging applications such as compression and denoising. Upon successful completion of this course, the student will be able to explain the importance and use of signal representations in building more sophisticated signal processing tools, such as wavelets think in basic timefrequency terms describe how Fourier theory fits in a bigger picture of signal representations use basic multirate building blocks, such as a twochannel filter bank characterize the discrete wavelet transform and its variations construct a timefrequency decomposition to fit a given signal explain how these tools are used in various applications and apply these concepts to solve a practical bioimaging problem through an independent project. Prerequisite 18791, or permission of instructor. Also known as 18799
Prerequisites: 18-396
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