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:
- Hough Transform, used to find lines in an image
- Radon Transform, used to reconstruct images from fan-beam and parallel-beam projection data
- 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
An effective approach to applying image transforms includes using a comprehensive environment for data analysis, visualization, and algorithm development. See MATLAB® and Image Processing Toolbox™ for more information.
Examples and How To
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Fourier Transforms (Blog)
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Examples of Various Geometric Transforms (Example)
Software Reference
- Image Transforms in Image Processing Toolbox (Documentation)
- Spatial Image Transforms in Image Processing Toolbox (Documentation)
- Filtering and Transforms (Function List)
- Image Processing Tools (MATLAB Apps)
- Integrating MATLAB and C/C++
See also: Image Processing Toolbox, Steve on Image Processing, image enhancement, digital image processing, image segmentation, geodesy, map projection, image analysis, geometric transformations and image registration, image processing and computer vision, feature extraction, optical flow, color profile, image analysis, image thresholding, edge detection, image registration, ransac, affine transformation, lab color
