Course Details

Subject {L-T-P / C} : EE4401 : Digital Image Processing { 3-0-0 / 3}

Subject Nature : Theory

Coordinator : Prof. Dipti Patra

Syllabus

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

Course Outcomes

At the end of the course, students will be able to <br />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. <br />2. Demonstrate a good understanding of the history and the current state-of-the-art image processing systems and applications. <br />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. <br />4. Implement image processing tasks with a high level of proficiency via software and hardware systems. <br />5. Identify potential applications of image processing to advancement of knowledge in sciences and engineering.

Essential Reading

Supplementary Reading

Journal and Conferences