Matrices, Colors, and Filters

 In this chapter, we will cover the following recipes:

• Manipulating matrices-creating, filling, accessing elements, and ROIs
• Converting between different data types and scaling values
• Non-image data persistence using NumPy
• Manipulating image channels
• Converting images from one color space to another
• Gamma correction and per-element math
• Mean/variance image normalization
• Computing image histograms
• Equalizing image histograms
• Removing noise using Gaussian, median, and bilateral filters
• Computing gradient images using Sobel filters
• Creating and applying your own filter
• Processing images with real-valued Gabor filters
• Going from the spatial to the frequency domain (and back) using discrete Fourier transform
• Manipulating image frequencies for image filtration
• Processing images with different thresholds
• Morphological operators
• Binary images-image masks and binary operations