中国科学院大学 计算机与控制学院

Teaching purposes & requirements: This is a basic specialized course for post graduates majored in Information and Communication
Engineering, Computer Science and Technology, Electronic Science and Technology, Control Science and Engineering, Electrical engineering. This course lays the foundation for advanced research or follow-up courses in digital image processing and related fields.
Preparatory courses: Advanced Mathematics, Linear Algebra, Stochastic Process, Digital Signal Processing

Chapter I “Introduction”, contents, methods and applications of the digital image processing; imaging system and visual system.
Chapter II “image digitalization”, one-dimensional sampling concepts; two-dimensional sampling theorem; image samples quantification; representation and storage of digital image.
Chapter III “image transformation”, discrete Fourier transformation; discrete cosine transformation; Walsh transformation; Hadamard transformation; wavelet transformation.
Chapter IV “image enhancement”, Spatial method; frequency method; homomorphism filtering method; color image processing.
Chapter V ”image compression and coding”, basic principle of image compression: image redundancy, fidelity criteria, information theory, lossless compression, lossy compression; static image coding; moving picture coding; advanced research.
Chapter VI “image restoration”, degradation model; unconstrained image restoration: inverse filter, recovery for image blurring; constrained image restoration: Wiener filter, least squares filter; de-noising recovery: median filter; other restoration techniques: geometric distortion correction, maximum entropy image restoration.
Chapter VII “image reconstruction”, basic principles and methods; the development and application of CT technology.
Chapter VIII “image analysis”, image segmentation, image description, object recognition.

"Digital Image Processing", Rafael, C. Gonzalez, and Richard E. Woods, 2nd Edition, Prentice Hall