Course Title: Image Processing (3 Cr.)
Course Code: CACS404
Class Load: 5 Hrs. / Week (Theory: 3Hrs. Practical: 2Hrs.)
This course presents an introduction to several topics on image processing techniques and their applications. It also explores the students’ real-world applications of image processing.
Upon completion of this course, students should be able to l. 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 them with the image enhancement, image restoration and image segmentation techniques.
Unit 1: Fundamental of Image processing [8 Hours]:
Image representation, basic relationship between pixels, elements of DIP system, elements of visual perception-simple image formation model, Sampling and Quantization, Color fundamentals and models, File Formats, Image operations. Brightness, contrast, hue, saturation, Mach band effect
Unit 2: Image Enhancement [12 Hours]:
Image Transforms, Fourier Transform and Discrete Fourier Transform, Fast Fourier Transform. Cosine Transform, Frequency domain image enhancement, low pass filtering, high pass filtering, homomorphic filter, Gaussian filter. – Spatial domain image enhancement, point processing, contrast stretching, clipping and thresholding, digital negative, intensity level slicing. Histogram processing: equalization, modification, Spatial filtering averaging, Smoothing and sharpening, median filtering, spatial low, high and band pass filters
Unit 3: Image Restoration [9 Hours]:
Image Restoration – Image degradation model – Noise modeling – Blur, Inverse filtering- removal of blur caused by uniform linear motion, Wiener filtering, Morphological operation, erosion and dilation,
Unit 4: Image coding and compression [9 Hours]:
Need for compression, redundancy, pixel coding, run length coding, Hufknancoding, Elements of information theory, Error free compression, Lossy compression,Image compression standards- JPEG & MPEG, wavelet based image compression.
Unit 5: Image segmentation and feature extraction [10 Hours]:
Image Segmentation: Thresholding, Region based segmentation, edges, lines and curve detection,edge operators, Image Features and Extraction, Texture, Feature reduction algorithms,Image classification, clustering techniques.
Case Studies in Image Security, Steganography and Digital watermarking, Visual effects, Case studies in Medical Imaging and remote sensing.
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.
l. Gonzalez Rafel C, Digital Image Processing, Pearson Education, 2009.
2. S.Sridhar, Digital Image Processing, Oxford University Press, 2011
1. Milan Sonka, Vaclav Hlavac and Roger Boyle, Image Processing, Analysis and Machine Vision, Second Edition, Thompson Learning, 2007
To download full Syllabus CLICK HERE