Unit 1: Fundamental of Image processing [8 Hours]
Image representation, basic relationship between pixels, elements of DIP system, elements of visual perception-simple image formation model, Sampling and Quantization, Color fundamentals and models, File Formats, Image operations. Brightness, contrast, hue, saturation, Mach band effect
Unit 2: Image Enhancement [12 Hours]
Image Transforms, Fourier Transform and Discrete Fourier Transform, Fast Fourier Transform. Cosine Transform, Frequency domain image enhancement, low pass filtering, high pass filtering, homomorphic filter, Gaussian filter. – Spatial domain image enhancement, point processing, contrast stretching, clipping and thresholding, digital negative, intensity level slicing. Histogram processing: equalization, modification, Spatial filtering averaging, Smoothing and sharpening, median filtering, spatial low, high and band pass filters
Unit 3: Image Restoration [9 Hours]
Image Restoration – Image degradation model – Noise modeling – Blur, Inverse filtering- removal of blur caused by uniform linear motion, Wiener filtering, Morphological operation, erosion and dilation,
Unit 4: Image coding and compression [9 Hours]
Need for compression, redundancy, pixel coding, run length coding, Hufknancoding, Elements of information theory, Error free compression, Lossy compression, Image compression standards- JPEG & MPEG, wavelet based image compression.
Unit 5: Image segmentation and feature extraction [10 Hours]
Image Segmentation: Thresholding, Region based segmentation, edges, lines and curve detection, edge operators, Image Features and Extraction, Texture, Feature reduction algorithms, Image classification, clustering techniques.
Case Studies in Image Security, Steganography and Digital watermarking, Visual effects, Case studies in Medical Imaging and remote sensing.
Laboratory work should be done covering all the topics listed above and a small project work should be carried out using the concept learnt in this course using software like matlab, python.