Image Transform

Transform methods in image processing

An image transform can be applied to an image to convert it from one domain to another. Viewing an image in domains such as frequency or Hough space enables the identification of features that may not be as easily detected in the spatial domain. Common image transforms include:

• Radon transform, used to reconstruct images from fan-beam and parallel-beam projection data
• Hough transform, used to find lines in an image
• Discrete cosine transform, used in image and video compression
• Discrete Fourier transform, used in filtering and frequency analysis
• Wavelet transform, used to perform discrete wavelet analysis, denoise, and fuse images

You can apply image transforms in MATLAB with Image Processing Toolbox, which provide image transforms, tools, and a comprehensive environment for data analysis, visualization, and algorithm development.  The products also let you perform geometric transformations.

Examples and How To

• Image Processing Toolbox Overview 1:53 (Video)
• Fourier Transforms (Blog)
• Examples of Various Geometric Transforms (Example)
• Reconstructing an Image from Projection Data (Example)

Software Reference

• Image Transforms in Image Processing Toolbox (Documentation)
• Spatial Image Transforms in Image Processing Toolbox (Documentation)
• Filtering and Transforms (Function List)

See also: Steve on Image Processing, image enhancement, digital image processing, image segmentation, geodesy, map projection, image analysis, geometric transformations and image registration, image and video processing