Unit 1 Introduction to Digital Image Processing

4 lecture hours

• What is Digital Image Processing? Origins and history of digital image processing Fields that use digital image processing (Medical
• Remote Sensing
• etc.) Fundamental steps in digital image processing Components of an image processing system

Unit 2 Digital Image Fundamentals

5 lecture hours

• Elements of visual perception Light and the electromagnetic spectrum Image sensing and acquisition Image sampling and quantization Basic relationships between pixels (neighbors
• connectivity) Basic mathematical tools (linear vs. non-linear operations
• arithmetic/logic operations)

Unit 3 Intensity Transformations and Spatial Filtering

6 lecture hours

• Basic intensity transformation functions (log
• contrast stretching) Histogram processing (equalization
• specification/matching) Fundamentals of spatial filtering and filter mechanics Smoothing (lowpass) spatial filters: mean
• order-statistics Sharpening (high pass) spatial filters: Laplacian
• unsharp masking

Unit 4 Filtering in the Frequency Domain

7 lecture hours

• Fourier series and Fourier transform background Sampling and the Fourier transform of sampled functions Discrete Fourier Transform (DFT) – 1D and 2D Properties of the 2-D DFT and IDFT Image smoothing using lowpass frequency domain filters (Ideal
• Gaussian) Image sharpening using high pass filters Selective filtering: band reject and bandpass filters Fast Fourier Transform (FFT) – efficiency and implementation

Unit 5 Color Image Processing

5 lecture hours

• Color fundamentals: tristimulus values
• chromaticity Color models: RGB, CMY/CMYK, HSI, HSV, Lab Pseudo color image processing Basics of full-color image processing Color transformations and color balance Color image smoothing and sharpening

Unit 6 Wavelet and Other Image Transforms

• Matrix-based transforms (basis vectors and images) 05 Program Name: Bachelor of Engineering Level: UG Subject Code : BE05000191 Subject Name : Digital Image Processing Discrete Cosine Transform (DCT)
• Walsh-Hadamard Transform Haar Transform Wavelet transforms: multiresolution analysis
• fast wavelet transform 2-D wavelet transforms and sub band coding

Unit 7 Morphological Image Processing

4 lecture hours

• Structuring elements
• erosion and dilation Opening and closing operations Hit-or-miss transform Morphological algorithms: boundary extraction
• region filling
• pruning Morphological reconstruction Grayscale morphology

Unit 8 Image Segmentation

• Fundamentals of image segmentation Edge detection: point
• line, and edge detectors (Sobel
• Laplacian of Gaussian) Thresholding: global
• variable, and multivariable thresholding
• Otsu's method Region-based segmentation: growing
• splitting, and merging K-means clustering
• super pixels Graph-cut segmentation Morphological watersheds