Crack detection in simply supported beams without baseline modal parameters by stationary wavelet transform

A novel speckle reduction technique based on soft thresholding of wavelet coefficients using interval type II fuzzy system was developed for reducing speckle noise in Optical Coherence Tomography images. The proposed algorithm is an extension of a recently published method for filtering additive Gaussian noise by use of type I fuzzy system. Unlike type I, interval type II fuzzy based thresholding filter considers the uncertainty in the calculated threshold and the wavelet coefficient is adjusted based on this uncertainty. A single parameter controls the signal-to-noise (SNR) improvement. Application of this novel algorithm to optical coherence tomograms acquired in-vivo from a human finger tip show reduction in the speckle noise with little edge blurring and image SNR improvement of about 10dB. Comparison with adaptive Wiener and adaptive Lee filters, applied to the same image, demonstrated the superior performance of the fuzzy type II algorithm in terms of image metrics improvement.

Speckle noise reduction algorithm for optical coherence tomography based on interval type II fuzzy set

Majority of the existing copy-move forgery
 detection algorithms operate based on the principle of image block matching. However, such detection becomes complicated when an intelligent adversary blurs the edges of forged region(s). To solve this problem, the authors present a novel approach for detection of copy-move forgery using stationary wavelet transform (SWT)
 which, unlike most wavelet transforms (e.g. discrete wavelet transform), is shift invariant, and helps in finding the similarities, i.e. matches and dissimilarities, i.e. noise, between the blocks of an image, caused due to blurring. The blocks are represented by features extracted using singular value decomposition (SVD)
 of an image. Also, the concept of colour-based segmentation
 used in this work helps to achieve blur invariance. The authors' experimental results prove the efficiency of the proposed method in detection of copy-move forgery involving intelligent edge blurring. Also, their experimental results prove that the performance of the proposed method in terms of detection accuracy is considerably higher compared with the state-of-the-art.

Blur-invariant copy-move forgery detection technique with improved detection accuracy utilising SWT-SVD

Wavelet transform is widely used in feature extraction of magnetic resonance imaging. However, the traditional discrete wavelet transform (DWT) suffers from translation variant property, which may extract significantly different features from two images of the same subject with only slight movement. In order to solve this problem, this paper utilizes stationary wavelet transform (SWT) to extract features instead of DWT. Experiments on a normal brain MRI demonstrate that wavelet coefficients via SWT is superior to that via DWT concerning translation invariant property.

Feature Extraction of Brain MRI by Stationary Wavelet Transform

Abnormal or uncontrollable growth of a tissue within the brain is known as a brain tumor. The tumor detection, classification and investigate by experts is a usual technique for brain tumor classification from MRI. Explicit classification of brain MRI and interpretation of brain images organized and various techniques has been proposed. The proposed method for classification of Brain Tumor using Classifier and Neural Network uses segmentation algorithms which include Skull Striping along with morphological operation aiming that preprocessing, Anisotropic Diffusion Filtering uses for noise removal and smoothing with edge detection locating the boundaries anatomically of the image. Secondly, wavelet transforms used in the decomposition and the feature extraction of MRI and combining features of MRI images. Finally, Adaboost uses classification techniques, learn by selecting discriminative features proposed to classify MRI images into tumorous (malignant) and non-tumorous (benign). Experimental results show 100% accuracy for 155 MRI images.

V. Sadasivam

Papers

Undecimated wavelet based speckle reduction for SAR images