Kind
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Title
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Lecture 1
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Introduction, Fundamental steps in Digital Image Processing
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Lecture 2
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Elements of Visual Perception, Image sampling and quantization.
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Lecture 3
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Some basic relationships between pixels, some basic gray level transformations.
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Lecture 4
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Histogram Processing, Basics of Spatial filtering.
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Lecture 5
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Smoothing spatial filters, sharpening spatial filters.
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Lecture 6
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Two-dimensional Fourier transform.
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Lecture 7
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Homomorphic filtering and implementations.
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Lecture 8
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Image degradation models and Restoration
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Lecture 9
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Periodic noise reduction in frequency domain
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Exam
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First Project Presentation
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Lecture 10
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Inverse filtering , Minimum Mean-square filtering, Geometric Transformation.
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Lecture 11
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Color transformation, Color Enhancement.
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Lecture 12
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Wavelet and Multi-resolution image Processing
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Exam
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Second Project Presentation
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Lecture 13
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Image Compression Models
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Lecture 14
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Elements of Information Theory
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Lecture 15
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Morphological Image Processing
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Lecture 16
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Image Segmentation
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Exam
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Third Project Presentation
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