Introduction to the basic digital image processing concepts; understanding of the algorithms for image enhancement, restoration, morphological processing, compression, and segmentation.

Understanding of theoretical foundations of the basic digital image processing techniques and ability to design simple digital image processing systems. Ability to implement digital image processing algorithms for image enhancement, or for noise and degradation removal. Theoretical and implementation knowledge of image segmentation algorithms.Student is expected to easily extend and acquire new knowledge by working on a specific problem.

Introduction to digital image processing (application examples, basic components of image processing systems). Basic concepts in image processing (visual perception, image sensing and acquisition, sampling and quantization, relationships between pixels). Image improvement in space domain (intensity transformations, histogram, spatial filtering, smoothing, sharpening). Image improvement in frequency domain (2D Discrete Fourier Transform, properties, filtering in frequency domain). Image restoration (noise models,filtering for noise removal, estimation of the degradation function, inverse filtering, Wiener filter). Color image processing (color models, color transformations, color image processing, pseudo-color image processing, color segmentation). Image compression (redundancy in images, basic lossless compression methods, predictive coding, transformation coding). Morphological image processing (basic morphological image operations and algorithms for binary and grey scale images). Image segmentation (point, line, edge detection, thresholding).

Lectures; Computer Practice; Consultations.