Edge detection in noisy images using directional diffusion with log filters
Image noise reduction and edge detection have been important image processing techniques. Traditional isotropic image smoothing reduces noise at the cost of image blurring. Anisotropic smoothing tries to maintain the image features, while reducing noise. This thesis presents an anisotropic smoothing implementation that only uses a 3-by-3 window, and therefore is easy to implement in hardware. Edge detection is also studied in this thesis. A pre-processing technique is proposed to do position dependent brightness correction. This technique makes edge thresholding easier. We also present an algorithm that implements Gaussian filtering more accurately than Gauss-Hermite integration at the cost of an insignificant increase in computing complexity.