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Image Processing

Image Processing focuses on the analysis and manipulation of digital images to extract useful information. It covers image enhancement, filtering, segmentation, feature extraction, and basic computer vision techniques used in areas such as medical imaging, surveillance, and multimedia applications.

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BSC-CSIT BCA-OLD BIT

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Image Processing

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Course Title: Image Processing

Course No: CACS404

Nature of the Course: Theory + Lab

Semester: 7

Full Marks: 20 + 20 + 60

Pass Marks: 8 + 8 + 24

Credit Hours: 3

Course Description

Course Objectives

Course Contents

1. Fundamental of Image processing

8 hrs

1.1. Image representation, basic relationship between pixels

1.2. Elements of DIP system, elements of visual perception

1.3. Simple image formation model

1.4. Sampling and Quantization

1.5. Color fundamentals and models

1.6. File Formats, Image operations

1.7. Brightness, contrast, hue, saturation, Mach band effect

2. Image Enhancement

12 hrs

2.1. Image Transforms, Fourier Transform and Discrete Fourier Transform

2.2. Fast Fourier Transform, Cosine Transform

2.3. Frequency domain image enhancement

2.4. Low pass filtering, high pass filtering, homomorphic filter

2.5. Gaussian filter

2.6. Spatial domain image enhancement

2.7. Point processing, contrast stretching, clipping and thresholding

2.8. Digital negative, intensity level slicing

2.9. Histogram processing: equalization, modification

2.10. Spatial filtering – averaging, Smoothing and sharpening

2.11. Median filtering, spatial low, high and band pass filters

3. Image Restoration

9 hrs

3.1. Image degradation model - Noise modeling

3.2. Blur, Inverse filtering - removal of blur caused by uniform linear motion

3.3. Weiner filtering

3.4. Morphological operation, erosion and dilation

4. Image coding and compression

9 hrs

4.1. Need for compression, redundancy, pixel coding

4.2. Run length coding, Huffman coding

4.3. Elements of information theory, Error free compression

4.4. Lossy compression

4.5. Image compression standards - JPEG & MPEG

4.6. Wavelet based image compression

5. Image segmentation and feature extraction

10 hrs

5.1. Image Segmentation: Thresholding

5.2. Region based segmentation

5.3. Edges, line and curve detection

5.4. Edge operators

5.5. Image Features and Extraction, Types of features

5.6. Feature extraction, Texture, Feature reduction algorithms

5.7. Image classification, clustering techniques

5.8. Case Studies in Image Security, Steganography and Digital watermarking

5.9. Visual effects, Case studies in Medical Imaging and remote sensing

Laboratory Works

1.Laboratory Works

Text Books

1.Gonzalez Rafel C, Digital Image Processing, Pearson Education, 2009
2.S.Sridhar, Digital Image Processing, Oxford University Press, 2011

Reference Books

1.Milan Sonka, Vaclav Hlavac and Roger Boyle, Image Processing, Analysis and Machine Vision, Second Edition, Thompson Learning, 2007

Notes:

Source:

This course presents introduction to several topics on image processing techniques and their applications. It also explores the students to real-world applications of image processing.

Upon completion of this course, students should be able to 1. Explain the basic concepts of digital image processing and various image transforms. 2. Develop a broad range of image processing techniques and their applications. 3. To familiarize the with the image enhancement, image restoration and image segmentation techniques.

Laboratory work should be done covering all the topics listed above and a small project work should be carried out using the concept learnt in this course using software like matlab, python.

This syllabus follows the official BCA curriculum of Tribhuvan University. In case of any doubt or revision, the university's published syllabus shall be considered authoritative.