Course Details
Subject {L-T-P / C} : EE6131 : Digital Image Processing { 3-0-0 / 3}
Subject Nature : Theory
Coordinator : Prof. Dipti Patra
Syllabus
Module 1: DIGITAL IMAGE FUNDAMENTALS & 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. [5 hours]
Module 2: IMAGE ENHANCEMENT IN SPATIAL DOMAIN: Some basic gray level transformations, Histogram processing, Smoothing and Sharpening spatial filters, IMAGE ENHANCEMENT IN FREQUENCY DOMAIN: Smoothing and Sharpening frequency domain filters, Homomorphic filtering. [7 hours]
Module 3: 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. [6 hours]
Module 4: IMAGE COMPRESSION: Image compression models, Loss-less and Lossy compression, MULTIRESOLUTION PROCESSING: Image pyramids, sub-band coding, Harr transform multi resolution, Wavelet transforms. [ 7 hours]
Module 5: MORPHOLOGICAL IMAGE PROCESSING: Dilation and erosion, Opening and closing, Some basic morphological algorithms, IMAGE SEGMENTATION: Boundary detection based methods, region-based methods, template matching, Hough transform, Mean shift, active contours, Use of motion in segmentation [6 hours]
Module 6: COLOUR IMAGE PROCESSING: Devices for Colour Imaging, Colour Image Storage and Processing, Colour Models, Colour Quantization RECENT DEVELOPMENTS. [4 hours]
Course Objectives
- Describe and explain basic principles of digital image processing.
- Design and implement algorithms that perform basic image processing (e.g. noise removal and image enhancement).
- Design and implement algorithms for advanced image analysis (e.g. image compression, image segmentation).
- Assess the performance of image processing algorithms and systems in various applications
Course Outcomes
On successful completion of this course students will be able to: <br />CO1: Demonstrate on broad range of fundamental image processing techniques and concepts (linear and non-linear filtering, denoising, deblurring etc.) <br />CO2: Utility of image compression techniques for storage and transmission purpose. <br />CO3: Demonstrate on broad range of image analysis techniques and concepts (edge detection, segmentation, morphological operators etc.) <br />CO4: Demonstrate on multiresolution image analysis techniques <br />CO5: Demonstrate on Colour imaging, Colour models, and Colour image processing. <br />CO6: Identify, demonstrate and apply their knowledge by analyzing image processing problems and employing (or proposing) effective solutions.
Essential Reading
- Rafael C Gonzalez, Richard E Woods, Digital Image Processing, Pearson Education 2014
- Anil K. Jain, Fundamentals of Digital Image Processing, PHI
Supplementary Reading
- R.C. Gonzalez, R.E. Woods, and S. L. Eddins, Digital Image Processing using MATLAB, Pearson Prentice-Hall
- J. R. Parker, Algorithms for Image Processing and Computer Vision, Wiley and Sons , 2nd edition 2010
Journal and Conferences
- IEEE Transaction on Image Processing, IEEE International Conference on Image Processing
- IET Image Processing, IEEE Conference on Computer Vision & Image Processing