Digital Image Processing
CMPE Degree: This course is Not Applicable for the CMPE degree.
EE Degree: This course is Not Applicable for the EE degree.
Lab Hours: 0 supervised lab hours and 0 unsupervised lab hours.
Technical Interest Group(s) / Course Type(s): Digital Signal Processing
Prerequisites: A course in digital signal processing (ECE4270 or equivalent).
Catalog DescriptionAn introduction to the fundamentals and the theory of multidimensional signal processing and digital image processing, including key applications in multimedia products and services including machine learning
Student OutcomesIn the parentheses for each Student Outcome:
"P" for primary indicates the outcome is a major focus of the entire course.
“M” for moderate indicates the outcome is the focus of at least one component of the course, but not majority of course material.
“LN” for “little to none” indicates that the course does not contribute significantly to this outcome.
1. ( Not Applicable ) An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics
2. ( Not Applicable ) An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors
3. ( Not Applicable ) An ability to communicate effectively with a range of audiences
4. ( Not Applicable ) An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts
5. ( Not Applicable ) An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives
6. ( Not Applicable ) An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions
7. ( Not Applicable ) An ability to acquire and apply new knowledge as needed, using appropriate learning strategies.
Strategic Performance Indicators (SPIs)
1. Introduction to multidimensional signal processing
2-D convolution and filtering
2-D discrete-time Fourier transform
2-D sampling and reconstruction
Wavelets and Directional Transforms
Optical Flow, Motion Estimation and Video Coding
Image Quality Assessment
Enhancement, Restoration, and Denoising
Retrieval and Similarity Indexes
Saliency and Attention Models
4. Machine Learning for Images
Intro. to Machine Learning and its Applications in Image Processing
Basis Functions for Images and Autoencoders
Fund. of Linear Classifiers and SVMs
Basics of CNNs and Recent Applications in Image Classifications
Scene Labeling (Supervised and Weakly Supervised)
Overview of LSTM, RNN, and NTM for Image-related Applications
5. Color Processing
Color Component Transportation
6. Recent Trends in Image Processing