An image enhancement method that reduces speckle noise and preserves edges is introduced. The method is based on a new nonlinear multiscale reconstruction scheme that is obtained by successively combining each coarser scale image with the corresponding modified interscale image. Simulation results are included to demonstrate the performance of the proposed method.

Respiratory sound (RS) signals carry significant information about the underlying functioning of the pulmonary system by the presence of adventitious sounds (ASs). Although many studies have addressed the problem of pathological RS classification, only a limited number of scientific works have focused on the analysis of the evolution of symptom-related signal components in joint time-frequency (TF) plane. This paper proposes a new signal identification and extraction method for various ASs based on instantaneous frequency (IF) analysis. The presented TF decomposition method produces a noise-resistant high definition TF representation of RS signals as compared to the conventional linear TF analysis methods, yet preserving the low computational complexity as compared to those quadratic TF analysis methods. The discarded phase information in conventional spectrogram has been adopted for the estimation of IF and group delay, and a temporal-spectral dominance spectrogram has subsequently been constructed by investigating the TF spreads of the computed time-corrected IF components. The proposed dominance measure enables the extraction of signal components correspond to ASs from noisy RS signal at high noise level. A new set of TF features has also been proposed to quantify the shapes of the obtained TF contours, and therefore strongly, enhances the identification of multicomponents signals such as polyphonic wheezes. An overall accuracy of 92.4±2.9% for the classification of real RS recordings shows the promising performance of the presented method.

This paper presents two novel corner detection methods for gray level images based on log-Gabor wavelet transform. The input image is decomposed at multiscales and along multi-orientations. In the first algorithm, the magnitude along the direction that is orthogonal to the gradient orientation represents the "cornerness" measurement. Using this detector, corners are detected and localized accurately. Meanwhile, this method provides us the magnitudes and orientations along the principal axes corresponding to the directions of the local eigenvectors of the local changes. This information is useful when we deal with the affine transform applications in matching problems. The second proposed method is based on log-Gabor wavelets and second moment matrix. The input image is decomposed by the log-Gabor wavelets at multiscales along multi-orientations. Then the components at different scales and orientations are projected onto the x and y axis and formulated into the second moment matrix. Finally, the smaller eigenvalue of the second moment matrix is used to detect corner points. Compared with the most famous Harris detector and the recently published Kovesi's detector, the proposed methods provide better performance while retaining the good characteristics, such as robustness to noise, isotropic responses etc. Furthermore, the proposed methods address three problems existing in Harris detector. First, Harris method is based on single scale information. The proposed methods are based on the multiscales instead of single scale. Second, the derealization problem existing in Harris detector is minimized by utilizing good localization property of log-Gabor wavelets. Third, Harris detector has certain problems to detect corners of higher order structures. Due to the multi-orientation decomposition of the proposed methods, higher order structures are well computed. Consequently, the proposed methods detect corners of higher order structures very well. The proposed methods are first compared with Harris method and Kovesi's method in a subjective way. Then a classical stereo matching system is adopted to evaluate the corner detectors in an objective way. Both of the evaluations demonstrate better performance of the proposed methods.

This paper proposes a new image watermarking technique, which adopts Independent Component Analysis (ICA) for watermark detection and extraction process (i.e., dewatermarking). Watermark embedding is performed in the spatial domain of the original image. Watermark can be successfully detected during the Principle Component Analysis (PCA) whitening stage. A nonlinear robust batch ICA algorithm, which is able to efficiently extract various temporally correlated sources from their observed linear mixtures, is used for blind watermark extraction. The evaluations illustrate the validity and good performance of the proposed watermark detection and extraction scheme based on ICA. The accuracy of watermark extraction depends on the statistical independence between the original, key and watermark images and the temporal correlation of these sources. Experimental results demonstrate that the proposed system is robust to several important image processing attacks, including some geometrical transformations—scaling, cropping and rotation, quantization, additive noise, low pass filtering, multiple marks, and collusion.