Digital Image Processing

Description:

Fundamentals of 2D signals and systems. Introduction to multidimensional signal processing. Applications in digital image processing. Image formation, representation and visualization. Image manipulation; point operations (single operand, dual operands). Linear and non-linear operators. Image restoration and wavelets. Orthogonal transforms. Applications in image analysis, enhancement, restoration, and coding.

Textbook:

Textbook: Digital Image Processing, 2nd Edition by Gonzalez and Woods Prentice Hall, 2002

Prerequisite:

ECE314 and ECE340, Elementary matrix theory

Coordinator:

Ramiro Jordan

Topics:

• 1. Introduction. What Is Digital Image Processing? The Origins of Digital Image Processing. Examples of Fields that Use Digital Image Processing. Fundamental Steps in Digital Image Processing. Components of an Image Processing System.
• 2. Digital Image Fundamentals. Elements of Visual Perception. Light and the Electromagnetic Spectrum. Image Sensing and Acquisition. Image Sampling and Quantization. Some Basic Relationships Between Pixels. Linear and Nonlinear Operations.
• 3. Image Enhancement in the Spatial Domain. Background. Some Basic Gray Level Transformations. Histogram Processing. Enhancement Using Arithmetic/Logic Operations. Basics of Spatial Filtering. Smoothing Spatial Filters. Sharpening Spatial Filters. Combining Spatial Enhancement Methods.
• 4. Image Enhancement in the Frequency Domain. Background. Introduction to the Fourier Transform and the Frequency Domain. Smoothing Frequency-Domain Filters. Sharpening Frequency Domain Filters. Homomorphic Filtering. Implementation.
• 5. Image Restoration. A Model of the Image Degradation/Restoration Process. Noise Models. Restoration in the Presence of Noise Only-Spatial Filtering. Periodic Noise Reduction by Frequency Domain Filtering. Linear, Position-Invariant Degradations. Estimating the Degradation Function. Inverse Filtering. Minimum Mean Square Error (Wiener) Filtering. Constrained Least Squares Filtering. Geometric Mean Filter. Geometric Transformations.
• 6. Color Image Processing. Color Fundamentals. Color Models. Pseudocolor Image Processing. Basics of Full-Color Image Processing. Color Transformations. Smoothing and Sharpening. Color Segmentation. Noise in Color Images. Color Image Compression.
• 7. Wavelets and Multiresolution Processing. Background. Multiresolution Expansions. Wavelet Transforms in One Dimension. The Fast Wavelet Transform. Wavelet Transforms in Two Dimensions. Wavelet Packets.
• 9. Morphological Image Processing. Preliminaries. Dilation and Erosion. Opening and Closing. The Hit-or-Miss Transformation. Some Basic Morphological Algorithms. Extensions to Gray-Scale Images.
• 10. Image Segmentation. Detection of Discontinuities. Edge Linking and Boundary Detection. Thresholding. Region-Based Segmentation. Segmentation by Morphological Watersheds. The Use of Motion in Segmentation.
• 11. Representation and Description. Representation. Boundary Descriptors. Regional Descriptors. Use of Principal Components for Description. Relational Descriptors.
• 12. Object Recognition. Patterns and Pattern Classes. Recognition Based on Decision-Theoretic Methods. Structural Methods. Rafael C. Gonzalez and Richard E. Woods, "Digital Image Processing," Second Edition, by Addison Wesley, 2002