Abstract
The imperfections of image acquisition systems produce noise. The majority of edge detectors, including gradient-based edge detectors, are sensitive to noise. To reduce this sensitivity, the first step of some edge detectors’ algorithms, such as the Canny’s edge detector, is the filtering of acquired images with a Gaussian filter. We show experimentally that this filtering is not sufficient in case of strong Additive White Gaussian or multiplicative speckle noise, because the remaining grains of noise produce false edges. The aim of this paper is to improve edge detection robustness against Gaussian and speckle noise by preceding the Canny’s edge detector with a new type of denoising system. We propose a two-stage denoising system acting in the Hyperanalytic Wavelet Transform Domain. The results obtained in applying the proposed edge detection method outperform state-of-the-art edge detection results from the literature.
Highlights
One of the key operations in remote sensing image processing is edge detection
Comparing the results obtained by applying the Hyperanalytic Wavelet Transform (HWT)-Bishrink denoising association with the results of the proposed denoising method, we suggest that the idea of two stages and the proposed improvements of the bishrink filter are effective, and further, our algorithm is faster than Synthetic Aperture Radar (SAR)-BM3D and H-BM3D algorithms
The HWT transform is flexible, four times redundant, and quasi-invariant to translation, with good directional selectivity, which is useful in many signal and image processing applications
Summary
One of the key operations in remote sensing image processing is edge detection. The result of edge detection is a binary image representing a sketch of that image which carries important information concerning the localization of different targets into the remote sensing image. We can use edge detection mostly for general image processing goals such as segmentation, detection of boundary or recognition of objects. Edges are high frequency components of an image, localized where sharp transitions of the image intensity appear. Edge detectors transform a grayscale or color image into a binary image known as an edge map, which preserves almost all the information content carried by the original image. The edge map represents a sparse form of the original image with a reduced number of bits, favorable for further fast processing tasks
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