This chapter describes the methods used to prepare images for further analysis. A simple taxonomy of general image processing operations for points, lines, and regions is developed to guide the discussion, with background for filter design using spatial kernels. We discuss color management systems and necessary methods for color processing as a part of preparing images for analysis, including colorimetric spaces, as well as color-based segmentation and color space optimizations, approximations, and color reductions. Fourier processing for image analysis and enhancements is covered for 1D, 2D, and 3D cases, as well as a general discussion of related transforms such as Slant, Hadamaard, and Walsh. Morphology methods are presented, along with an introduction to super pixels. We survey segmentation methods using neural networks, such as the U-Net architecture, and the history of segmentation using neural networks, beginning with region proposals using Mask-RCNN using CNN’s. In addition, we examine a wider range of texture metrics useful for color enhancements. Various methods for object detection are introduced, such as Yolo.