This chapter introduces the principles of Fourier analysis for image processing, shifting from visually intuitive spatial-domain methods to frequency-based representations. By decomposing signals into sinusoidal components, the Fourier transform reveals how low and high frequencies correspond to smooth regions and fine details in an image. The chapter first explains one-dimensional frequency transformation, then extends the concept to two-dimensional images and the discrete Fourier transform used in digital computation. Fundamental operations such as spectrum interpretation, filtering in the frequency domain, and the relationship between convolution and multiplication are also presented to build a practical understanding of frequency-domain image processing.