Novel wavelet domain Wiener filtering de-noising techniques: Application to bowel sounds captured by means of abdominal surface vibrations

Joint Wavelet Video Denoising and Motion Activity Detection in Multimodal Human Activity Analysis: Application to Video-Assisted Bioacoustic/Psychophysiological Monitoring

“…Focusing on the demands of the current humanactivity video-surveillance system, noise worsens the quality of the acquired images, produces erroneous estimations of the motion-activity parameters, and deteriorates the video compression efficiency. Video denoising, as it happens with all single-sided signal restoration techniques [14,30,31], try to estimate the noise statistical attributes from the available noise-contaminated signal, in order to apply spatiotemporal filtering. In addition, autonoise estimation methods have been proposed to facilitate unsupervised image and video denoising [14][15][16][17][18][31][32][33][34][35].…”

“…Wiener filter, which minimizes the mean-square error between the original clean signal and the estimated one obtained during the reconstruction procedure, is the basis for the current denoising approach. Thus, extending the 1D processing case [30], the Wiener filtering operation in the frequency-space domain is described by the following equation [14,31,35]:…”

“…Equation (2) describes the so-called 2D parametric Wiener filter, where the c WF parameter is used to control the amount of noise suppression and it may be omitted in the simplest case of classical Wiener filter (c WF = 1) [30,31]. The "∼" symbol, which is used in the F S∼ (ω i , ω j ), P N∼ (ω i , ω j ) components of (2) denotes that the corresponding signals are estimations of the original ones (clean image spectrum F S and noise power P N ), since the latter are not available.…”

“…In addition, we introduce the wavelet noise power that has been extracted during the previous frame denoising, to refine the final noise levels that would be involved in the Wiener filtering. An ExpMA iterative procedure has been selected for the noise estimation process, since it proved very efficient in 1D processing [30], as well as because the whole motion detection process utilizes ExpMA structures:…”

“…where a N is the corresponding ExpMA constant (0 < a N < 1), also called memory term [30], J N∼4 (w i , w j , n − 1) is the previous-frame noise estimation (extracted after the (n − 1)-frame denoising has been completed) and J N∼1 (w i , w j , n) is the noise extracted during the first-level denoising of the empirical Wiener filter. The factor k m might be different at various scales, so we use the generic expression k m for all (l; AD)| DWT .…”