Image Processing Syllabus - BCA (TU)

Course Description

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.

Course objectives

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.

Course Contents

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

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.

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