Combined wavelet domain and motion compensated filtering compliant with video codecs

In this paper, we introduce the idea of using motion estimation resources from a video codec for video denoising. This is not straightforward because the motion estimators aimed for video compression and coding, tolerate errors in the estimated motion field and hence are not directly applicable to video denoising. To solve this problem, we propose a novel motion field filtering step that refines the accuracy of the motion estimates to a degree that is required for denoising. We illustrate the use of the proposed motion estimation method within a wavelet-based video denoising scheme. The resulting video denoising method is of low-complexity and receives comparable results with respect to the latest video denoising methods

Wavelet-based denoising for 3D OCT images

Optical coherence tomography produces high resolution medical images based on spatial and temporal coherence of the optical waves backscattered from the scanned tissue. However, the same coherence introduces speckle noise as well; this degrades the quality of acquired images. In this paper we propose a technique for noise reduction of 3D OCT images, where the 3D volume is considered as a sequence of 2D images, i.e., 2D slices in depth-lateral projection plane. In the proposed method we first perform recursive temporal filtering through the estimated motion trajectory between the 2D slices using noise-robust motion estimation/compensation scheme previously proposed for video denoising. The temporal filtering scheme reduces the noise level and adapts the motion compensation on it. Subsequently, we apply a spatial filter for speckle reduction in order to remove the remainder of noise in the 2D slices. In this scheme the spatial (2D) speckle-nature of noise in OCT is modeled and used for spatially adaptive denoising. Both the temporal and the spatial filter are wavelet-based techniques, where for the temporal filter two resolution scales are used and for the spatial one four resolution scales. The evaluation of the proposed denoising approach is done on demodulated 3D OCT images on different sources and of different resolution. For optimizing the parameters for best denoising performance fantom OCT images were used. The denoising performance of the proposed method was measured in terms of SNR, edge sharpness preservation and contrast-to-noise ratio. A comparison was made to the state-of-the-art methods for noise reduction in 2D OCT images, where the proposed approach showed to be advantageous in terms of both objective and subjective quality measures

Video denoising by fuzzy motion and detail adaptive averaging

A new fuzzy-rule-based algorithm for the denoising of video sequences corrupted with additive Gaussian noise is presented. The proposed method constitutes a fuzzy-logic-based improvement of a recent detail and motion adaptive multiple class averaging filter (MCA). The method is first explained in the pixel domain for grayscale sequences, and is later extended to the wavelet domain and to color sequences. Experimental results show that the noise in digital image sequences is efficiently removed by the proposed fuzzy motion and detail adaptive video filter (FMDAF), and that the method outperforms other state of the art filters of comparable complexity on different video sequences

Semiautomatic epicardial fat segmentation based on fuzzy c-means clustering and geometric ellipse fitting

Automatic segmentation of particular heart parts plays an important role in recognition tasks, which is utilized for diagnosis and treatment. One particularly important application is segmentation of epicardial fat (surrounds the heart), which is shown by various studies to indicate risk level for developing various cardiovascular diseases as well as to predict progression of certain diseases. Quantification of epicardial fat from CT images requires advance image segmentation methods. The problem of the state-of-the-art methods for epicardial fat segmentation is their high dependency on user interaction, resulting in low reproducibility of studies and time-consuming analysis. We propose in this paper a novel semiautomatic approach for segmentation and quantification of epicardial fat from 3D CT images. Our method is a semisupervised slice-by-slice segmentation approach based on local adaptive morphology and fuzzy c-means clustering. Additionally, we use a geometric ellipse prior to filter out undesired parts of the target cluster. The validation of the proposed methodology shows good correspondence between the segmentation results and the manual segmentation performed by physicians

Waveletdomain video denoising based on reliability measures

Abstract—This paper proposes a novel video denoising method based on nondecimated wavelet band filtering. In the proposed method, motion estimation and adaptive recursive temporal filtering are performed in a closed loop, followed by an intra-frame spatially adaptive filter. All processing occurs in the wavelet domain. The paper introduces new wavelet-based motion reliability measures. We make a difference between motion reliability per orientation and reliability per wavelet band. These two reliability measures are employed in different stages of the proposed denoising scheme. The reliability per orientation (horizontal and vertical) measure is used in the proposed motion estimation scheme while the reliability of the estimated motion vectors (MVs) per wavelet band is utilized for subsequent adaptive temporal and spatial filtering. We propose a novel cost function for motion estimation which takes into account the spatial orientation of image structures and their motion matching values. Our motion estimation approach is a novel wavelet-domain three-step scheme, where the refinement of MVs in each step is determined based on the proposed motion reliabilities per orientation. The temporal filtering is performed separately in each wavelet band along the estimated motion trajectory and the parameters of the temporal filter depend on the motion reliabilities per wavelet band. The final spatial filtering step employs an adaptive smoothing of wavelet coefficients that yields a stronger filtering at the positions where the temporal filter was less effective. The results on various grayscale sequences demonstrate that the proposed filter outperforms several state-of-the-art filters visually (as judged by a small test panel) as well as in terms of peak signal-to-noise ratio. Index Terms—Motion estimation, video denoising, wavelets. I

Noise estimation for video processing based on spatio-temporal gradients

Abstract—We propose an efficient and accurate wavelet-based noise estimation method for white Gaussian noise in video sequences. The proposed method analyzes the distribution of spatial and temporal gradients in the video sequence in order to estimate the noise variance. The estimate is derived from the most frequent gradient in the two distributions and is compensated for the errors due to the spatio–temporal image sequence content, by a novel correction function. The spatial and temporal gradients are determined from the finest scale of the spatial and temporal wavelet transform, respectively. The main application of the noise estimation algorithm is in wavelet-based video processing. The results show that the proposed method is more accurate than other state-of-the-art noise estimation techniques and less sensitive to varying spatio–temporal content and noise level. Index Terms—Noise estimation, video analysis and processing, wavelets. I