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]

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

On successful completion of this course students will be able to:
CO1: Demonstrate on broad range of fundamental image processing techniques and concepts (linear and non-linear filtering, denoising, deblurring etc.)
CO2: Utility of image compression techniques for storage and transmission purpose.
CO3: Demonstrate on broad range of image analysis techniques and concepts (edge detection, segmentation, morphological operators etc.)
CO4: Demonstrate on multiresolution image analysis techniques
CO5: Demonstrate on Colour imaging, Colour models, and Colour image processing.
CO6: Identify, demonstrate and apply their knowledge by analyzing image processing problems and employing (or proposing) effective solutions.

Rafael C Gonzalez, Richard E Woods, Digital Image Processing, Pearson Education 2014

Anil K. Jain, Fundamentals of Digital Image Processing, PHI

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

IEEE Transaction on Image Processing, IEEE International Conference on Image Processing

IET Image Processing, IEEE Conference on Computer Vision & Image Processing