Review of matrices - Vector spaces - Probability and random variables - Origin of digital image processing: Examples of fields that use digital image processing - Elements of visual perception brightness and contrast - Image sensing and acquisition - Image sampling and quantization - Some basic relationships between pixels. Image Enhancement in spatial domain: Some basic gray level transformations - Histogram processing - Enhancement using arithmetic/logic operations - Basics of spatial filtering - Smoothing and sharpening spatial filters
Image enhancement in frequency domain: Review of sampling and discrete fourier transform - Image enhancement in the frequency domain. Frequency domain filtering: Smoothing – Sharpening - Homomorphic filtering. Color image processing fundamentals: Pseudo color image processing- Basics of full color image processing.
Image Compression: Image compression models – Error free compression- Lossy compression – Applications of image compression. Image transforms: Introduction to transformation used for image processing applications -Cosine – Hadamard – Haar – Sine - KL Transforms and their properties.