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Abstract
Fixed size kernels are used to extract differential structure of images. Increasing the kernal size reduces the localization accuracy and noise along with increase in computational complexity. The computational cost of edge extraction is related to the image resolution or scale. In this paper wavelet scale correlation for edge detection along with scalability in edge detector has been envisaged. The image is decomposed according to its resolution, structural parameters and noise level by multilevel wavelet decomposition using Quadrature Mirror Filters (QMF). The property that image structural information is preserved at each decomposition level whereas noise is partially reduced within subbands, is being exploited. An innovative wavelet synthesis approach is conceived based on scale correlation of the concordant detail bands such that the reconstructed image fabricates an edge map of the image. Although this technique falls short to spot few edge pixels at contours but the results are better than the classical operators in noisy scenario and noise elimination is significant in the edge maps keeping default threshold constraint.
Highlights
Spatial domain, frequency domain and wavelet based techniques are being used independently to detect edges in an image
Fourier transform being global in nature can neither localize sharp transients nor differentiate between true and false edges under noisy scenario
A multiscale edge detection algorithm has been presented in [4] for SAR images but it is not advocated for the low PSNR images the two major dilemmas for edge detection are; firstly the choice of appropriate threshold [5] to segregate noise and true edges and secondly to opt for an appropriate scale for edge detection
Summary
Frequency domain and wavelet based techniques are being used independently to detect edges in an image. Fourier transform being global in nature can neither localize sharp transients nor differentiate between true and false edges under noisy scenario. The classical edge detectors [1,2] do not yield adequate edge maps of the noisy images over default threshold values. A good threshold assigned to yield a good edge map for a particular type of image and noise model may be inappropriate for other type of image or the different noise model. It requires user’s intervention to assign suitable threshold value to differentiate between true and false edges. A multiscale edge detection algorithm has been presented in [4] for SAR images but it is not advocated for the low PSNR images the two major dilemmas for edge detection are; firstly the choice of appropriate threshold [5] to segregate noise and true edges and secondly to opt for an appropriate scale for edge detection
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