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Lecture# Image Filtering: Basics and Techniques

Description

This lecture covers the fundamentals of image filtering, including linear and nonlinear filters, their applications in biological images, and techniques for artifact/noise removal, background subtraction, and feature enhancement. It explains spatial filtering, which manipulates pixel data to improve image aspects like contrast and feature enhancement. The lecture also discusses point operations for modifying pixel values without altering image structure, and provides examples of linear filters such as mean and Gaussian filters. Nonlinear filters like Gaussian and median kernels are explored, along with their applications. The instructor emphasizes the differences between linear and nonlinear filters, highlighting their advantages and performance characteristics.

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Instructors (4)

Image Processing and Analysis for Life Scientists

This course intends to teach image analysis/processing with a strong emphasis
of applications in life sciences. The idea is to enable the participants to solve
image processing questions via workflo

Image Processing and Analysis for Life Scientists

This course intends to teach image analysis/processing with a strong emphasis
of applications in life sciences. The idea is to enable the participants to solve
image processing questions via workflo

Registration details will be announced via email. It takes place from September to December & intends to teach image processing with a strong emphasis of applications in life sciences. The idea is to

Median filter

The median filter is a non-linear digital filtering technique, often used to remove noise from an image or signal. Such noise reduction is a typical pre-processing step to improve the results of later processing (for example, edge detection on an image). Median filtering is very widely used in digital because, under certain conditions, it preserves edges while removing noise (but see the discussion below), also having applications in signal processing.

Gaussian filter

In electronics and signal processing, mainly in digital signal processing, a Gaussian filter is a filter whose impulse response is a Gaussian function (or an approximation to it, since a true Gaussian response would have infinite impulse response). Gaussian filters have the properties of having no overshoot to a step function input while minimizing the rise and fall time. This behavior is closely connected to the fact that the Gaussian filter has the minimum possible group delay.

Wiener filter

In signal processing, the Wiener filter is a filter used to produce an estimate of a desired or target random process by linear time-invariant (LTI) filtering of an observed noisy process, assuming known stationary signal and noise spectra, and additive noise. The Wiener filter minimizes the mean square error between the estimated random process and the desired process. The goal of the Wiener filter is to compute a statistical estimate of an unknown signal using a related signal as an input and filtering that known signal to produce the estimate as an output.

Kalman filter

For statistics and control theory, Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, including statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by estimating a joint probability distribution over the variables for each timeframe. The filter is named after Rudolf E. Kálmán, who was one of the primary developers of its theory.

Gaussian blur

In , a Gaussian blur (also known as Gaussian smoothing) is the result of blurring an by a Gaussian function (named after mathematician and scientist Carl Friedrich Gauss). It is a widely used effect in graphics software, typically to reduce and reduce detail. The visual effect of this blurring technique is a smooth blur resembling that of viewing the image through a translucent screen, distinctly different from the bokeh effect produced by an out-of-focus lens or the shadow of an object under usual illumination.

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