> ## Documentation Index > Fetch the complete documentation index at: https://aegean.ai/llms.txt > Use this file to discover all available pages before exploring further. # Images as Signals > Classical image processing, from linear filtering and sampling to the statistics of natural images and the priors built on them. Before any network learns a filter, an image is a signal, and the classical tools of signal processing already explain much of what vision needs. This chapter starts with linear filtering: convolution, blurring, and derivatives, then extends them to time and to sampling. The image priors that follow describe what natural images have in common and turn that knowledge into denoising, texture synthesis, and inference with graphical models. Each section is written by a student of the course from a chapter of *Foundations of Computer Vision* by Torralba, Isola, and Freeman, with the book's figures linked rather than reproduced. ### Filtering and sampling Linear translation-invariant systems, convolution, boundary handling, and template matching. Box, Gaussian, and binomial filters and their frequency responses. Discrete and Gaussian derivatives, the Laplacian, sharpening, and Retinex. A video as a space-time volume, velocity-tuned blur, and velocity-nulling filters. The sampling theorem, reconstruction, sampling lattices, and anti-aliasing. ### Image priors The 1/f power law, heavy-tailed derivatives, and denoising with image priors. Texture synthesis by histogram matching and by copying neighborhoods. Markov random fields and belief propagation for denoising, segmentation, and stereo. *** [Edit this page on GitHub](https://github.com/aegean-ai/eaia/edit/main/src/aiml-common/lectures/image-processing/index.mdx) or [file an issue](https://github.com/aegean-ai/eaia/issues/new/choose).