CSc 470 / I0807
Image Processing

Course description

This course is an intensive introduction to image processing intended for advanced undergraduates and graduate students. It forms the basis of high-quality rendering in computer graphics, as well as low-level processing for computer vision, remote sensing, and medical imaging. You will learn about well-known image processing topics, including

Point operations:

thresholding, quantization, clipping, gamma correction, contrast enhancement

Neighborhood operations:

blurring, sharpening, convolution, halftoning, error diffusion

Fourier transforms

discrete Fourier transform (DFT), fast Fourier transform (FFT)

Digital filtering

sampling theory, image resampling, image reconstruction, antialiasing

Geometric operations

spatial transformations, separable warping, 2-pass scanline algorithms, affine and perspective transformations, texture mapping, morphing

Course goals

Understand the concepts of point, neighborhood, and geometric operations as they apply to digital images.
Understand frequency analysis and its role in filtering and sampling theory.
Understand the concepts of convolution, interpolation, and antialiasing to solve geometric transformation problems.
Use programming assignments to reinforce the material covered in class.
Implement operations efficiently using a high-level language (e.g., C++).
Apply the concept of separability for efficient implementation.

Piazza

This term we will be using Piazza for class discussion.
The system is highly catered to getting you help fast and efficiently from classmates, the TA, and myself.
Rather than emailing questions to the teaching staff, I encourage you to post your questions on Piazza.
If you have any problems or feedback for the developers, email team@piazza.com.
Find our class page here

Zoom

The Zoom link for the lectures and office hours can be found on the Piazza page (see above).
Make sure to add your name to the enrollment on the Piazza page to access all messages and Q/A postings.

Sample code

Click here for code examples useful for homework and implementation details of algorithms discussed in lectures.

Sample images

Click here for sample images that you may use for your homework.

Homework submission

Please follow the instructions here for turning in homework solutions.

Syllabus

LECTURE	LECTURE TOPICS	HOMEWORK	SLIDES
1: 9/8/26	Introduction, history, applications		CSc470-01-Introduction.pdf
2: 9/10/26	Human visual system		CSc470-02-HVS.pdf
3: 9/15/26	Digital image fundamentals		CSc470-03-Fundamentals.pdf
4: 9/17/26	Point operations		CSc470-04-PointOps.pdf
5: 9/22/26	Point operations (contd)	Qt information for homeworks
6: 9/24/26	Arithmetic/Logic Operations		CSc470-05-ArithLogicOps.pdf
7: 9/29/26	Digital halftoning		CSc470-06-Halftoning.pdf
8: 10/1/26	Digital halftoning (contd)
9: 10/6/26	Neighborhood operations		CSc470-07-NeighborhoodsOps.pdf
10: 10/8/26	Neighborhood operations (contd)		Image padding notes
11: 10/15/26	Neighborhood operations (contd)
12: 10/20/26	Filtering Theory		CSc470-08-Filtering.pdf
13: 10/22/26	Filtering Theory (contd)
14: 10/27/26	Fourier Transform		CSc470-09-Fourier.pdf
15: 10/29/26	Midterm review
16: 11/3/26	Fourier Transform (contd)
17: 11/5/26	Filtering in the Frequency Domain		CSc470-10-FreqDomainFiltering.pdf
18: 11/10/26	Midterm
19: 11/12/26	Sampling Theory		CSc470-11-SamplingTheory.pdf
20: 11/17/26	Image Resampling		CSc470-12-Resampling.pdf
21: 11/19/26	Image Reconstruction		CSc470-13-Reconstruction.pdf
22: 11/24/26	Antialiasing		CSc470-14-Antialiasing.pdf
23: 12/1/26	Spatial Transformations		CSc470-15-SpatialTransformations.pdf
24: 12/3/26	Image Warping		CSc470-16-Warping.pdf
25: 12/8/26	Image Morphing		CSc470-17-Morphing.pdf
27: 12/10/26	Final Review

Instructor

Professor George Wolberg
Office Hours: Tuesday, 1:00pm-2:00pm, Room NAC 8/202G
wolberg@ccny.cuny.edu

Teaching Assistant

Siavash Zokai
Office Hours: By appointment, online
ccny.cs470@gmail.com

Lectures

CSc 470: Tuesday/Thursday, 2:00pm-3:15pm, Room: NAC 7/305

Textbooks

Main Text: Image Processing and Analysis, by Stan Birchfield, Cengage Learning, 2018.
Supplementary Text: Digital Image Processing by Rafael Gonzalez and Richard Woods, Pearson, 2018.
Supplementary Text: Image Processing, Analysis, and Machine Vision by Milan Sonka, Vaclav Hlavac, and Roger Boyle, Cengage Learning, 2014.
Supplementary Text: Digital Image Warping by George Wolberg, IEEE Computer Society Press, 1990.

Supplemental Videos

Watch the brilliant video series by 3Blue1Brown on Youtube that cover convolution , Fourier transforms, and Fourier series..
3Blue1Brown created his own visualization software to animate his lessons, which extend beyond the videos mentioned above. He has videos on a wide variety of topics in math, which impacts image processing, computer graphics, neural networks for machine learning, and more. His linear algebra videos are classic. Definitely check them out!

Grading

Your final grade is computed as a weighted sum of the programming projects, a midterm exam, and a final exam.

4 programming projects (50%)
Midterm exam (25%)
Final exam (25%)
Good class participation is a bonus

George Wolberg, August 4, 2026

CSc 470 / I0807 Image Processing