Linear Image Processing

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.
CHAPTER
Linear Image Processing
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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