Part 1 builds the essential mathematical, algorithmic, and computational foundations required for all subsequent topics in the book. It begins with the fundamentals of digital images, including representation, sampling, quantization, and image formation, and progresses toward practical image manipulation using Python. Readers are introduced to core programming workflows using NumPy, PIL, OpenCV, and scikit-image for reading, transforming, and visualizing images. The part then develops a strong theoretical and applied understanding of spatial and frequency-domain analysis. Topics include Fourier transforms, discrete convolution, correlation, filtering theory, and the relationship between spatial and spectral representations of images. Emphasis is placed on understanding how image structure can be analyzed and modified using mathematical operators.
Finally, classical image enhancement and filtering techniques are introduced, including smoothing, sharpening, and basic restoration concepts. By the end of this part, readers will have a solid foundation in both the mathematical principles and practical tools required to work effectively with digital images and prepare for more advanced computer vision and deep learning concepts.
This part contains the following chapters: