Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Lecture

Image Processing I: Filters and Transformations

In course

MICRO-511: Image processing I

Introduction to the basic techniques of image processing. Introduction to the development of image-processing software and to prototyping using Jupyter notebooks. Application to real-world examples in industrial vision and biomedical imaging. ...

Description

This lecture covers the implementation of moving average filters, including recursive implementation in 1D and separable transfer functions. It also discusses symmetric exponential filters, exponential filtering implementation, and linear scale-space concepts. The instructor explains the Central-limit theorem, efficient Gaussian filtering, and the nomenclature of prototypical filters. The lecture concludes with a summary on discrete images, Fourier transforms, digital filtering, and popular image-processing filters.

This video is available exclusively on Mediaspace for a restricted audience. Please log in to MediaSpace to access it if you have the necessary permissions.

Watch on Mediaspace

Instructors (2)

Dimitri Nestor Alice Van De Ville

Michaël Unser

Official source

Ontological neighbourhood

Information engineering

Artificial intelligence: Computer vision

Related concepts (32)

Low-pass filter

A low-pass filter is a filter that passes signals with a frequency lower than a selected cutoff frequency and attenuates signals with frequencies higher than the cutoff frequency. The exact frequency response of the filter depends on the filter design. The filter is sometimes called a high-cut filter, or treble-cut filter in audio applications. A low-pass filter is the complement of a high-pass filter. In optics, high-pass and low-pass may have different meanings, depending on whether referring to the frequency or wavelength of light, since these variables are inversely related.

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.

Scale space implementation

In the areas of computer vision, and signal processing, the notion of scale-space representation is used for processing measurement data at multiple scales, and specifically enhance or suppress image features over different ranges of scale (see the article on scale space). A special type of scale-space representation is provided by the Gaussian scale space, where the image data in N dimensions is subjected to smoothing by Gaussian convolution.

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.

Gaussian function

In mathematics, a Gaussian function, often simply referred to as a Gaussian, is a function of the base form and with parametric extension for arbitrary real constants a, b and non-zero c. It is named after the mathematician Carl Friedrich Gauss. The graph of a Gaussian is a characteristic symmetric "bell curve" shape. The parameter a is the height of the curve's peak, b is the position of the center of the peak, and c (the standard deviation, sometimes called the Gaussian RMS width) controls the width of the "bell".

Related lectures (45)

Digital Filtering Fundamentals

Introduces fundamental notions in digital filtering, covering 2D filtering approaches, linear filters, stability, FIR and IIR filters, frequency domain filtering, and Gaussian filters.

Image Filtering: Basics and Techniques MOOC: Image Processing and Analysis for Life Scientists

Explores image filtering techniques, including linear and nonlinear filters, for artifact removal and feature enhancement.

Digital Filtering: Butterworth Filter Design

Explores digital Butterworth filter design, covering scaling, stable poles, and filter application.

Fast Fourier Transform (FFT): Lecture 4

Covers the Fast Fourier Transform (FFT) algorithm, interpolation, filters, image processing, and experimental techniques in TEM and STM.

Image Filtering Introduction MOOC: Image Processing and Analysis for Life Scientists