Wavelet Based Boundary Detection

The purpose of this paper is to develop an algorithm for denoising images corrupted with additive white Gaussian noise (AWGN) with a view to extract object’s boundary. The noise degrades quality of the images and makes interpretations, analysis and segmentation of images harder. A pixel is said to be a boundary pixel if its deleted neighborhood contains at least one point from the object and one point from the object’s complement. The discrete wavelet transform using scale correlation is a denoising approach that reveals boundary pixels more effectively than the simple wavelet decomposition. The detail coefficients in concordant bands are correlated and then synthesized after soft thresholding, which suppresses noise but signifies smooth intensity variations. The wavelet coefficients of noise have much trivial correlation than the wavelet coefficients of boundaries that propagate along the scale. Scale multiplication improves the localization accuracy significantly while keeping high detection efficiency. The combination of noise filtering coupled with boundary detection in a single algorithm enables disconnected boundary detection in a noisy scenario. Curve fitting or cubic spline can then augment the boundaries to estimate missing pixels.