Image Processing

What you'll learn

The key areas this subject covers, mapped to the official curriculum.

• Digital image fundamentals: sampling, quantization and pixel relationships
• Spatial and frequency domain enhancement: histograms, convolution and Fourier filtering
• Image restoration and compression: noise models, Wiener filtering and JPEG/DCT
• Morphological operations: dilation, erosion, opening and closing
• Segmentation and recognition: edges, thresholding, descriptors and classification

Course units

The structured path through this subject.

1. 1

   Introduction

   What a digital image is, how it is sensed, sampled and quantized, and the pixel-level relationships that later algorithms build on.

   5 topics · 5 lessons available

2. 2

   Image Enhancement and Filter in Spatial Domain

   Point, histogram and neighborhood operations that improve image appearance directly on pixel values.

   5 topics · 5 lessons available

3. 3

   Image Enhancement in the Frequency Domain

   Representing images as sums of sinusoids, filtering them through the Fourier domain, and doing it fast with the FFT.

   4 topics · 4 lessons available

4. 4

   Image Restoration and Compression

   Modeling degradation and noise, recovering the original image, and removing redundancy to store images compactly.

   4 topics · 4 lessons available

5. 5

   Introduction to Morphological Image Processing

   Probing binary images with structuring elements to extract shape information through set operations.

   3 topics · 3 lessons available

6. 6

   Image Segmentation

   Partitioning an image into meaningful regions using edges, thresholds, regions and watersheds.

   4 topics · 4 lessons available

7. 7

   Representations, Description and Recognition

   Summarizing segmented regions, describing shape and texture, and assigning labels with a classifier.

   4 topics · 4 lessons available