A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Characterization of Signals from Multiscale Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge characterization using normalized edge detector
CVGIP: Graphical Models and Image Processing
Edge and Curve Detection for Visual Scene Analysis
IEEE Transactions on Computers
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Image denoising: a nonlinear robust statistical approach
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Analysis of multiscale products for step detection and estimation
IEEE Transactions on Information Theory
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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.