The Scientist and Engineer's Guide to
Digital Signal Processing
By Steven W. Smith, Ph.D.

Chapter 24: 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.

• Convolution
• 3x3 Edge Modification
• Convolution by Separability
• Example of a Large PSF: Illumination Flattening
• Fourier Image Analysis
• FFT Convolution
• A Closer Look at Image Convolution