This is a very common application for noise removal, to obtain a sharp image.
In this course we will continue to apply and enhance the work done in class 1, but we will also introduce a range of approaches to noise removal, from simple Unionization methods to more sophisticated non-uniformly spaced noise removal. To avoid confusion, we will have many examples used to explain the more complicated methods. We will go over the following general topics:
What is Unionization?
How is it different from normal Gradient Descent?
How to train random forest models?
How to reduce noise to sharpen blurred images?
How is noise spread in a noise vector?
How can we create a high-quality image from a noisy source?
The most common application of non-uniformly spaced noise reduction methods is for noise levels that are often not evenly spread in a noisy image. These images may also have a range of noise levels, as they could be clipped during the noise removal process during pixel reconstruction.
Other topics to be covered include:
Image quality estimation in practice
Clocking and image quality analysis techniques
Concepts like image sharpening
Clocking and sharpening image quality analysis techniques
Other Info:
Assignment 1: (Pascal A2.
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CS472 / CM472 / CS672 Fall 2006: Assignment 2 Instructor: Pascal Part O CE: DC2514 Classroom: RCH305 WWF 4:00-5:20 Website: www.student.cs.uwaterloo.ca/ cs472/ Email: popular cs.Waterloo.ca O CE Hours:
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