课程名称 (Course Name) ： Digital Image Processing

课程代码 (Course Code）： F033572

学分/学时 (Credits/Credit Hours)： 3/54

开课时间 (Course Term )：  Autumn

开课学院（School Providing the Course）:  SEIEE

任课教师（Teacher）:  Lu HONGTAO

课程讨论时数（Course Discussion Hours）:  0

课程实验数（Lab Hours）:   0

课程内容简介(Course Introduction):

    This course aims to introduce the basic theory, principles and methods for digital image processing. The contents of this course consist of digital image fundamentals, image enhancement in spatial domain, image enhancement in frequency domain, Image restoration, Color image processing, Multiresolution and  Wavelets, Morphological processing, Image segmentation, etc.

教学大纲(Course Teaching Outline):

This course will tell you the basic concepts, methods and algorithms for digital image processing. After having learned this course and accomplished the projects, you will have a comprehensive understanding of digital image processing and will become familiar with some typical processing methods of digital images. The course also serves as a foundation for further studying of pattern recognition and computer vision. The materials that will be covered in this course include digital image fundamentals, image enhancement in spatial domain, image enhancement in frequency domain, Image restoration, Color image processing, Multiresolution and  Wavelets, Morphological processing, Image segmentation, etc.

课程进度计划(Course Schedule):

Week 1  Digital Image Fundamentals: What is digital image processing, History of Digital Image Processing, Applications of Image Processing, Imaging Modalities

Week 2  Image Enhancement in Spatial Domain: Gray scale transformation, Histogram equalization, Spatial filters, Image smoothing filters, Image Sharpening Filters.

Week 3  Image enhancement in frequency domain: brief introduction of linear system theory, Fourier Transform, lowpass filters in frequency domain—Ideal, Butterworth and Gaussian filters.

Week 4 Image enhancement in frequency domain (continued): Highpass filtersing in the frequency domain---Ideal, Butterworth and Gaussain, bandpass and band stop filters in the frequency domain---ideal, Butterworth and Gaussian. Image restoration: degradation model.

Week 5  Image restoration (continued): random noise, periodic noise, mean filers, ordered statistical filters, Wiener filters.

Week 6 Color image processing: color fundamentals, primary and secondary colors for light and pigments, color models---RGB, CMY, HIS, Pseudo-color processing, full color processing.

Week 7 Wavelets and multiresolution processing: Image pyramid, subband coding, Haar transform, multiresolution expansion—scaling functions and wavelet functions, wavelet transformation, fast wavelet transformation.

Week 8 Image compression: discrete image transforms, linear transform, unitary transform, orthogonal transform, discrete cosine transform, discrete sine transform, slant transform, Haar transform. Redundancy in image, run length coding, Huffman coding, Transform coding—DCT, DWT, Image compression standards.

Week 9 Morphological processing: set theory, erosion, dilation, opening, closing, hit-or-miss, boundary extraction, hole filling, connected component extraction, convex hull, thinning, thickening, skeleton. Extension to gray level images.

Week 10  Image segmentation: point detection, line detection, edge detection, Hough transform, thresholding, region growing, watershed algorithm.

Week 11  Image representation: boundary following, chain codes, polygon approximation, signatures, boundary segments, some simple descriptors, Fourier descriptors, statistical moments, textures, PCA.

课程考核要求(Course Assessment Requirements)：

There are several projects after class, that covers the main topics in the course. The assessment is based on the quality of the projects and the reports.

参考文献(Course References)：

1. Digital Image Processing (Third Edition)， Rafael C. Gonzalez, Richard E. Woods，2010.

2. DIGITAL IMAGE PROCESSING,  Kenneth R. Castleman, 2008.

预修课程（Prerequisite Course）

Linear algebra, Matrix theory, Probability and statistics