Contourlet

About: Contourlet is a research topic. Over the lifetime, 3533 publications have been published within this topic receiving 38980 citations.

Coupling denoising algorithm based on discrete wavelet transform and modified median filter for medical image

Abstract: In order to overcome the phenomenon of image blur and edge loss in the process of collecting and transmitting medical image, a denoising method of medical image based on discrete wavelet transform (DWT) and modified median filter for medical image coupling denoising is proposed. The method is composed of four modules: image acquisition, image storage, image processing and image reconstruction. Image acquisition gets the medical image that contains Gaussian noise and impulse noise. Image storage includes the preservation of data and parameters of the original image and processed image. In the third module, the medical image is decomposed as four sub bands (LL, HL, LH, HH) by wavelet decomposition, where LL is low frequency, LH, HL, HH are respective for horizontal, vertical and in the diagonal line high frequency component. Using improved wavelet threshold to process high frequency coefficients and retain low frequency coefficients, the modified median filtering is performed on three high frequency sub bands after wavelet threshold processing. The last module is image reconstruction,which means getting the image after denoising by wavelet reconstruction. The advantage of this method is combining the advantages of median filter and wavelet to make the denoising effect better, not a simple combination of the two previous methods. With DWT and improved median filter coefficients coupling denoising, it is highly practical for high-precision medical images containing complex noises. The experimental results of proposed algorithm are compared with the results of median filter, wavelet transform, contourlet and DT-CWT, etc. According to visual evaluation index PSNR and SNR and Canny edge detection, in low noise images, PSNR and SNR increase by 10%–15%; in high noise images, PSNR and SNR increase by 2%–6%. The experimental results of the proposed algorithm achieved better acceptable results compared with other methods, which provides an important method for the diagnosis of medical condition.

Analysis of watermarking framework for color image through a neural network-based approach

Abstract: In the research presented here, the general idea of watermarking framework is analyzed to deal with color image under a set of attacks through a neural network-based approach. It is realized in the area of transformation, especially with a focus on contourlet transform to address the proposed technique, as long as the bands of the suitable coefficients are accurately chosen. In summary, there is the logo information that is embedded in the edge of color image, while the Zenzo edge detector is correspondingly realized to handle the approach. In fact, the edge of the second subband is acquired, and subsequently, the capability of the above-referenced edge is calculated. A number of techniques are discussed to cope with the above-captioned watermarking framework through the new integration of contourlet transform in association with the multilayer perceptron to extract the logo information, appropriately. The approaches of the embedding and the de-embedding in case of learning algorithm of the aforementioned neural network through individual training data set are considered in the present research to carry out a series of experiments with different scenarios for the purpose of verifying the effectiveness of the proposed approach, obviously.