An Adaptive Gradient Edge Detection Algorithm Based on Wavelet Transformation

The image edge detection is one of the basic contents in image manipulation and analysis.Against the deficiency of traditonal single edge detection algorithms,e.g.,low anti-noise capacity,discontinuous edge and etc.,this paper proposes a new edge detection method combing two algorithms.Firstly,the original image is transformed by multi-layer wavelet decomposition to obtain respective approximate low frequency and detailed high coefficients.Secondly,for the low frequency part of wavelet decomposition image,an eight-point neighborhood adpative gradient algorithm is used for edge detection,and edge growing is used to ensure edge continuities.For the high frequency part,a wavelet transform partial max algorithm is used to detect the image edge.Next,the edge information of all the layers are combined as a certain rule to get the final edge of image.The results indicate that compared with the traditional edge detection algorithms this proposed method has the advantages of higher precision and signal-to-noise ratio,and it can make image edge extraction more accurate.