This course is an introduction to the basic concepts as well as applications of the rapidly
emerging field of digital image processing. It familiarizes the audience with the understanding,
design, and implementation of algorithms in the various subareas of digital image processing
such as image enhancement, image deblurring, image understanding, image security, and image
compression. Over 200 image examples complement the technical descriptions.
Benefits/Learning Objectives
This course will enable you to
explain the fundamental concepts and terminologies employed in digital imaging such as
sampling and aliasing, perceptual quantization; filtering, look-up tables, image histogram,
etc;

explain the various techniques used in image enhancement for contrast manipulation (e.g.,
histogram equalization), sharpening (e.g., unsharp masking) and noise removal (e.g.,
selective averaging, median filtering);
briefly demonstrate the performance of image deblurring algorithms such as inverse filtering
and Wiener filtering by using image examples;
briefly demonstrate the concepts behind digital signatures for image authentication and
invisible watermarking for image copyright protection;
briefly describe the current research topics in image understanding and demonstrate related
algorithm performances using image examples;
explain the basic technologies that serve the existing JPEG and the emerging JPEG2000
standards.
Intended Audience
Scientists, engineers, and managers who need to understand and/or apply the fundamental
concepts and techniques employed in digital image processing. Although no particular background is
needed, some prior knowledge of linear system theory (e.g., Fourier transforms) would be helpful.

1, deblurring  
n. [计] 去模糊
v. 由模糊变清晰；擦掉…的污点（deblur的ing形式）

2, histogram  ['histəugræm]
n. 柱状图
[计算机] 直方图

3, perceptual  [pə'septjuəl]
a. 感性的,知觉的

4, quantization  [,kwɔntai'zeiʃən]
量子化,数字化

5, Fourier  ['furiei]
n. 傅里叶