COMMON ELECTIVES 16EC401 DIGITAL IMAGE PROCESSING 3/ 0 / 0 / 3 : C(Theory Concept) Nature of Course Pre requisites : 17EC316 - Digital signal processing Course Objectives: 1 To provide knowledge about the basic steps and mathematical transforms in image processing. 2 To give a view on visual perception. 3 To learn various techniques to improve the visual appeal of the image. 4 To analyse the and technique to remove noise from the degraded images. 5 Understand the implementation of algorithm for image analysis. Course Outcomes: Upon completion of the course, students shall have ability to CXXX.1 Have an idea about the building blocks of a digital image processing system, and learn about the different types of images to be processed [R] in the course as well as the type of problems to be solved. CXXX.2 Learn the principles of image formation, sampling, quantization and the human visual system, which will allow them to investigate specific [U] image processing techniques later on. CXXX.3 Understand various image intensity transformations and spatial [U] filtering for the purpose of image enhancement. CXXX.4 Learn how to deal with different types of noise models, degradation [U] processes and filtering techniques for restoration. CXXX.5 Be of the difference between the various compression and segmentation algorithms and the types of objects (size and shape) [AP] they each produce. CXXX.6 Acquire an appreciation for the image processing issues and techniques and be able to apply these techniques to real world [AN] problems. Course Contents: Elements of a Digital Image Processing system Structure of Human eye Image formation – – and contrast sensitivity -Sampling and Quantization Neighbours of a pixel Distance – – measures Image Transforms: DFT algorithm transform Hadamard – –FFT –Walsh – transform Discrete Cosine transform-Introduction to wavelet transforms. Image – Enhancement: Basic Gray Level Transformations - Histogram equalization and specification technique–Smoothening and sharpening in spatial domain Smoothening and sharpening in – frequency domain Homomorphic filtering- Color image processing basics. Image – Restoration: Model Of Image Degradation/Restoration Process - Noise Models Filters for – Restoration in the Presence of Noise, Periodic Noise Reduction by Frequency Domain Filtering - Inverse Filtering -Wiener Filtering - Geometric Mean Filter. Image Compression : Image Compression Models Compression Arithmetic coding, LZW, RLE –Lossless –Huffmann,
COMMON ELECTIVES 16EC401 DIGITAL IMAGE PROCESSING 3/ 0 / 0 / 3 Nature of Course : C(Theory Concept) Pre requisites : 17EC316 - Digital signal processing Course Objectives: 1 To provide knowledge about the basic steps and mathematical transforms in image processing. 2 To give a view on visual perception. 3 To learn various techniques to improve the visual appeal of the image. 4 To analyse the and technique to remove noise from the degraded images. 5 Understand the implementation of algorithm for image analysis. Course Outcomes: Upon completion of the course, students shall have ability to CXXX.1 Have an idea about the building blocks of a digital image processing system, and learn about the different types of images to be processed in the course as well as the type of problems to be solved. [R] CXXX.2 Learn the principles of image formation, sampling, quantization and the human visual system, which will allow them to investigate specific image processing techniques later on. [U] CXXX.3 Understand various image intensity transformations and spatial filtering for the purpose of image enhancement. [U] CXXX.4 Learn how to deal with different types of noise models, degradation processes and filtering techniques for restoration. [U] CXXX.5 Be of the difference between the various compression and segmentation algorithms and the types of objects (size and shape) they each produce. [AP] CXXX.6 Acquire an appreciation for the image processing issues and techniques and be able to apply these techniques to real world problems. [AN] Course Contents: Elements of a Digital Image Processing system – Structure of Human eye – Image formation and contrast sensitivity -Sampling and Quantization – Neighbours of a pixel – Distance measures – Image Transforms: DFT –FFT algorithm –Walsh transform – Hadamard transform – Discrete Cosine transform-Introduction to wavelet transforms. Image Enhancement: Basic Gray Level Transformations - Histogram equalization and specification technique–Smoothening and sharpening in spatial domain – Smoothening and sharpening in frequency domain – Homomorphic filtering- Color image processing basics. Image Restoration: Model Of Image Degradation/Restoration Process - Noise Models – Filters for Restoration in the Presence of Noise, Periodic Noise Reduction by Frequency Domain Filtering - Inverse Filtering -Wiener Filtering - Geometric Mean Filter. Image Compression : Image Compression Models –Lossless Compression –Huffmann, Arithmetic coding, LZW, RLE
R16%20syllabus.docx