Course Details

Subject {L-T-P / C} : EE4401 : Digital Image Processing {3-0-0 / 3}
Subject Nature : Theory
Coordinator : Prof. Dipti Patra


Digital image fundamentals and Transforms: Elements of visual perception, image sampling and quantization, basic relationship between pixels, basic geometric transformations, 2D Fourier Transform, DFT, FFT, Separable Image Transforms, Walsh – Hadamard, Discrete Cosine Transform, Haar, Slant – Karhunen – Loeve Transforms.
Image Enhancement: intensity transformations, contrast stretching, histogram equalization, correlation and convolution spatial domain filters: smoothing filters, sharpening filters Frequency domain filters: smoothing filters, sharpening filters, homomorphic filter.
Image Restoration: Model of Image Degradation/restoration process, Image deformation and geometric transformations, Noise models, inverse filtering, least mean square filtering, constrained least mean square filtering, Blind image restoration, Pseudo inverse, Singular value decomposition.
Image Compression: Lossless compression: Variable length coding, LZW coding, Bit plane coding, predictive coding, DPCM. Lossy Compression: Transform coding – Wavelet coding, Basics of Image compression standards: JPEG, MPEG, Basics of Vector quantization.
Wavelets and Multiresolution Processing: Image pyramids, sub-band coding, Harr transform multi resolution expression, Wavelet transforms.
Morphological Image Processing: Erosion, Dilation, Opening, Closing, Hit-or-Miss Transform, Boundary Detection, Hole filling, Connected components, convex hull, thinning, thickening, skeletons, pruning, Geodesic dilation, Geodesic erosion, reconstruction by dilation and erosion.
Image Segmentation: Boundary detection based methods, region-based methods, template matching, Hough transform, Mean shift, active contours, Use of motion in segmentation

Course Objectives

  1. Describe and explain basic principles of digital image processing.
  2. Design and implement algorithms that perform basic image processing (e.g. noise removal and image enhancement).
  3. Design and implement algorithms for advanced image analysis (e.g. image compression, image segmentation).
  4. Assess the performance of image processing algorithms and systems.

Course Outcomes

At the end of the course, students will be able to
1. Describe the basic issues and the scope (or principal applications) of image processing, and the roles of image processing and systems in a variety of applications.
2. Demonstrate a good understanding of the history and the current state-of-the-art image processing systems and applications.
3. Identify areas of knowledge which are required, select an appropriate approach to a given image processing task, and critically evaluate and benchmark the performance of alternative techniques for a given problem by simulation using, e.g., Matlab.
4. Implement image processing tasks with a high level of proficiency via software and hardware systems.
5. Identify potential applications of image processing to advancement of knowledge in sciences and engineering.

Essential Reading

  1. Rafael C Gonzalez, Richard E Woods, Digital Image Processing, Pearson Education 2003 , 3rd Edition
  2. A.K. Jain, Fundamentals of Digital Image Processing, PHI , New Edition

Supplementary Reading

  1. R.C. Gonzalez, R.E. Woods, and S. L. Eddins, Digital Image Processing using MATLAB, Pearson Prentice-Hall , 2004
  2. William K Pratt, Digital Image Processing, John Willey , new edition

Journal and Conferences

  1. IEEE Transaction on Image Processing, IEEE International Conference on Image Processing
  2. IET Image Processing, IEEE Conference on Computer Vision & Image Processing