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The Scientist and Engineer's
Guide to Digital Signal Processing
by Steven W. Smith
California Technical Publishing
ISBN 0-9660176-3-3 (1997)
Chapter 24. Linear Image Processing
- Convolution
- 3 3 Edge Modification
- Convolution by Separability
- Illumination Flattening
- Fourier Image Analysis
- FFT Convolution
- Summary of the key concept
Linear image processing is based on the same two techniques as conventional DSP:
convolution and Fourier analysis. Convolution is the more important
of these two, since images have their information encoded in the spatial domain rather
than the frequency domain. Linear filtering can improve images in many ways:
sharpening the edges of objects, reducing random noise, correcting for unequal
illumination, deconvolution to correct for blur and motion, etc. These procedures are
carried out by convolving the original image with an appropriate filter kernel, producing
the filtered image. A serious problem with image convolution is the enormous number
of calculations that need to be performed, often resulting in unacceptably long execution
times. This chapter presents strategies for designing filter kernels for various image
processing tasks. Two important techniques for reducing the execution time are also
described: convolution by separability and FFT convolution.
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