Ultrasound images often exhibit poor signal to noise ratio when compared with optical images, because of the presence multiplicative speckle noise. Speckle suppression is often carried out as a pre-processing step to aid in diagnosis using ultrasound images. In this study, dual tree complex wavelet transform-based Levy Shrink algorithm is proposed for denoising ultrasound images. The coefficients in each wavelet subband are modelled using a heavy tailed Levy distribution. The scale parameters of the Levy distribution are estimated using fractional moments. Within this framework, a Bayesian estimator is employed to denoise ultrasound images. The proposed Levy Shrink algorithm is verified using evaluation parameters such as peak signal to noise ratio, mean structural similarity index, correlation coefficient and equivalent number of looks. The efficiency of the proposed denoising algorithm is justified by conducting extensive experiments on real as well as simulated ultrasound images.
The recognition of the face from videos has numerous applications in Video Surveillances and Computer Vision. The main challenge of detecting face image in videos is the pose and the illumination variations and sudden changes in the movement of the object. The proposed system analyzes and recognizes the exact face image from the video even though there are pose variation and illumination variation while the existing systems deals with the recognition of the face images from still images. The image gradient value and the histogram values were calculated. These will be helpful for the identification of the face positions continuously in the video frame. The Hog features are extracted its used for identification of the particular person and Bhattacharya distance calculated. The results shows that the recognition rate of the proposed system is increased compared to other existing systems.
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