Study On The Key Technology Of Image Processing Based On N-Smoothlets

N-Smoothlets is an improvement of traditional Smoothlet transform. In recursive dyadic partitioning of image, there are most N edgelets being used to approach the texture of original image by N-Smoothlets, while there is only one edgelet in Smoothlet. N-Smoothlets can approach gometric characteristic of image block and decrease the geometric error by increasing the number of edgelet. N-Smoothlets can be considered as special Multismoothlets[1], the main difference between them is the approaching method. Traditional Multismoothlets transform can realize multiple approximations in each image dyadic block by a slightly translated support while N-Smoothlets can approach images more flexiblely. Comparatively speaking, N-Smoothlets has the advantage of better approximation for images with complex texture. However, N-Smoothlets can’t express the direction very well. The main contribution of this paper has two points, one is increasing the ability to express direction, the other is the application of N-Smoothlets for image denoising and edge detection. The main contents are as follows:1. Shear Smoothlet transform is proposed as traditional Smoothlet transform is restricted in the direction of image dyadic square. Considered that Smoothlet transform in different directions can adapt to the change of image texture better, direction characteristic of Shear operation is applied into Smoothlet transform to calculate the mean value of reconstructed images. Ultimately, the reconstructed image has characteristics of different directions. The block effect is decreased by calculating the mean value of reconstructed images.2. Image denosing algorithm based on N-Smoothlets is proposed. Traditional image denosing algorithm will lose amount of high-frequency information such as the edge information while removing noise in images, the mathmatics model removing Gaussian noise based on geometrical frame is studied by utilizing the characteristic that Gaussian noise has no fixed geometrical frame and the advantage of N-Smoothlets transform in extracting the geometrical frame. Furthre more, N-Smoothlets image denosing algorithm based on the mathmatics model is proposed.The optimal distortion rate parameter ? can be calculated by the relation between ? and variance of Gaussian noise. The proposed denosing algorithm can protect the high-frequency information in the image and decrease calculating complexity while removing noise.3. Image edge detection algorithm based on N-Smoothlets is proposed. NSmoothlets transform possesses the advantage of approaching edge in image in different direction and scale. The detection model and method based on N-Smoothlets transform is studied to detect puny and complex edges by importing the sliding N-Smoothlets window. N-Smoothlets transform can detect edge in image effectively because of the singularity of N-Smoothlets transform. Precision of edge detection based on NSmoothlets can be adjusted by setting threshold and shift of the sliding window.