Motion Compensated Deinterlacing With Efficient Artifact Detection for Digital Television Displays

Deinterlacing is a scanning-format conversion technique that can convert interlaced pictures to progressive ones. Progressive televisions or monitors intended for displaying conventional interlaced programs have to apply deinterlacing techniques as required. Motion adaptive deinterlacing has been used widely in various kinds of commercial products, but the picture quality in motion areas still has much room for improvement. Motion compensated deinterlacing solves this problem by interpolation along the motion trajectory and thus is becoming the main trend of the next generation of deinterlacing algorithms. However, it suffers visible artifacts caused by incorrect motion vectors. To improve such imperfect pictures, this paper presents a motion compensation algorithm with more accurate motion estimation and more efficient artifact detection for digital television displays. Soft-switching between intra and inter interpolations is realized by characterizing different areas of the picture. Simulation results indicate that the proposed scheme can produce high-quality pictures with less flicker and imperceptible artifacts than a number of other techniques that were tested. In particular, the simulated peak signal-to-noise ratio (peak SNR) is basically 3-10 dB better than a comparable and prominent deinterlacing method.     

Removal of ballistocardiogram artifacts from simultaneously recorded EEG and fMRI data using independent component analysis

Simultaneous recording of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) has been studied to identify areas related to EEG events. EEG data recorded in the magnetic resonance (MR) scanner with MR imaging is suffered from two specific artifacts, imaging artifact, and ballistocardiogram (BCG). In this paper, we focus on BCG. In preceding studies, average subtraction was often used for this purpose. However, average subtraction requires an assumption that BCG waveforms are precisely periodic, which seems unrealistic because BCG is a biomedical artifact. We propose the application of independent component analysis (ICA) with a postprocessing of high-pass filtering for the removal of BCG. With this approach, it is not necessary to assume that the BCG waveform is periodic. Empirically, we show that our proposed method removes BCG artifacts as well as does the average subtraction method. Power spectral density analysis of the two approaches shows that, with ICA, distortion of recovered EEG data is also as small as that associated with the average subtraction approach. We also propose a hypothesis for how head movement causes BCGs and show why ICA can remove BCG artifacts arising from this source.     

基于直方图信息和灰度偏移网格的数字减影图像灰度矫正算法

本文首次提出一种利用数字减影图像直方图信息和灰度偏移网格进行灰度矫正的新的矫正算法.该算法能够有效去除灰度失真带来的蒙盈片灰度级差异,使减影图像丢失的细节信息得以重现.通过分析减影图像直方图,提取其中的灰度偏移量信息,有效消除了减影图像中的灰度级噪声.首次提出灰度偏移网格,较好地解决了数字减影图像灰度矫正过程中减影质量与矫正速度的矛盾.     

Temporal Anti‐aliasing of a Stereoscopic 3D Video

Frequency domain analysis is a fundamental procedure for understanding the characteristics of visual data. Several studies have been conducted with 2D videos, but analysis of stereoscopic 3D videos is rarely carried out. In this paper, we derive the Fourier transform of a simplified 3D video signal and analyze how a 3D video is influenced by disparity and motion in terms of temporal aliasing. It is already known that object motion affects temporal frequency characteristics of a time‐varying image sequence. In our analysis, we show that a 3D video is influenced not only by motion but also by disparity. Based on this conclusion, we present a temporal anti‐aliasing filter for a 3D video. Since the human process of depth perception mainly determines the quality of a reproduced 3D image, 2D image processing techniques are not directly applicable to 3D images. The analysis presented in this paper will be useful for reducing undesirable visual artifacts in 3D video as well as for assisting the development of relevant technologies.     

Non-stationary content-adaptive projector resolution enhancement

Digital imagery and video can have content at higher resolutions than can be projected by most data projectors, which has led to a variety of techniques to improve the high-resolution perception from lower-resolution displays. However, the downsampling procedures frequently used to fit an original image or video of high-resolution into a lower-resolution projector cause a frustrating loss of fine structures in the projected imagery. Since the human visual system is more sensitive to certain image phenomena, such as text and edges, an optimal approach to preserving fine structures should further sharpen such displayed content. On the other hand, the human visual system is also very sensitive to aliasing effects in motion, such that over-sharpening can lead to significant motion artifacts.In this paper, a new non-stationary content-adaptive resolution enhancement scheme is proposed. Our main objective in this study is to reduce the severity of artifacts due to the image enhancement processes. To achieve this goal, distribution-based text detection and hypothesis-testing-based motion detection methods are developed. Three spatial kernels, each constructed using a new band-limited Wiener deconvolution filter, are used to enhance a given image with different sharpening strengths, where the differently enhanced images are combined using a weighted non-stationary filter. For evaluation, a new visual projection assessment (VPA) dataset along with new metrics for quantifying motion artifacts are introduced. Experimental results show that the proposed non-stationary content-adaptive resolution enhancement scheme offers improved visual quality over the state-of-the-art while offering a reasonable balance between high text sharpness and reduced motion artifacts.     

Correction of computed tomography motion artifacts using pixel-specific back-projection

Cardiac and respiratory motion can cause artifacts in computed tomography scans of the chest. The authors describe a new method for reducing these artifacts called pixel-specific back-projection (PSBP). PSBP reduces artifacts caused by in-plane motion by reconstructing each pixel in a frame of reference that moves with the in-plane motion in the volume being scanned. The motion of the frame of reference is specified by constructing maps that describe the motion of each pixel in the image at the time each projection was measured; these maps are based on measurements of the in-plane motion. PSBP has been tested in computer simulations and with volunteer data. In computer simulations, PSBP removed the structured artifacts caused by motion. In scans of two volunteers, PSBP reduced doubling and streaking in chest scans to a level that made the images clinically useful. PSBP corrections of liver scans were less satisfactory because the motion of the liver is predominantly superior-inferior (S-I). PSBP uses a unique set of motion parameters to describe the motion at each point in the chest as opposed to requiring that the motion be described by a single set of parameters. Therefore, PSBP may be more useful in correcting clinical scans than are other correction techniques previously described.     

Automated correction of patient motion and gray values prior to subtraction in digitized angiography

This paper deals with an automated method for the simultaneous correction of patient motion and gray values prior to subtraction in digitized angiography. The algorithm consists of maximizing the deterministic sign change (DSC) criterion with respect to three registration parameters (two translational shifts and one constant value added to the pixel values of the image with contrast medium). This method is proved to be very efficient to correct for patient motion artifacts and is computationally cheap. Its main advantage is to permit the use of regions of interest which include vascular structures for the registration procedure. This method can be proposed for a routine use on every commercial digitized angiographic system.     

Reverberation reduction in ultrasonic B-mode images via dual frequency image subtraction

The authors demonstrate the feasibility of an approach, dual-frequency subtraction imaging, for suppressing artifacts produced by reverberation of strong echoes among specular reflectors. This method is based upon the principle that specularly reflected echoes from flat boundaries are frequency-independent whereas the diffusely scattered echoes from small scatterers are frequency-dependent. The approach was assessed on phantoms including one consisting of two parallel plastic plates between layers of foam sponges using a prototype experimental system. Preliminary results show that this method is superior to simple thresholding techniques or signal compression and holds great promise for suppressing reverberation artifacts in ultrasonic images.     

Quantitative analysis of artifacts in linear space-invariant image restoration

Several image restoration algorithms exist in the literature ranging from deterministic iterative techniques to optimum recursive methods. Unfortunately, all these algorithms produce undesirable artifacts in the process of undoing the degradations because of the ill-posed nature of the image restoration problem. This paper provides a complete quantitative analysis of different artifacts caused by linear shift-invariant (LSI) image restoration methods. The aim of this paper is to mathematically show how these artifacts originate in the general case of an arbitrary blur point spread function and an arbitrary LSI restoration filter, and then to study the characteristics of these artifacts in the special cases of uniform motion blur and out-of-focus blur via experimental analysis. Several pictures that illustrate these artifacts are presented. We discuss strategies for the suppression of these artifacts based on the analysis provided.This paper is based upon research performed under NSF grants MIP-8809291 and CDA-8820693, and Grant No. 88-IJ-CX-0038 from the National Institute of Justice to the University of Rochester.     

Computed masks in coronary subtraction imaging

In this paper we propose a method to overcome the effects of cardiac and respiratory motion in coronary subtraction imaging. We present the ideas of retrospective gating of masks, where both cardiac and respiratory phases are measured for a set of masks, and are subsequently used in a functional decomposition of motion. Through retrospective gating of masks, we are able to select appropriate images and to perform temporal and spatial processing on them to produce computed masks, subtraction of which should lead to motion-artifact-free images. The computed masks are built from two components: the first includes the time-variant structures related to respiration, such as ribs and soft tissues of the chest wall, and the second incorporates the time-variant structures related to cardiac motion. A preliminary study of the method in the area of digital subtraction angiography produced images which are comparable to but not better than those produced by techniques in current clinical practice: we discuss the reasons for this.     

Flexible mask subtraction for digital angiography

A flexible-mask algorithm that effectively provides correction for motion artifacts and gray-level variations in digital subtraction angiography (DSA) images is presented. The algorithm makes use of a flexible local registration of the mask with the live image to minimize the main distortions caused by the complex motion of the heart, namely spatial translation, rotation, and nonisotropic scaling. It also reduces the gray-level variations caused by the diffusion of the contrast medium into parts of the heart other than the arteries. It is concluded from experiments on X-ray coronary angiograms that when the background near the arteries is rugged or provides significant interference, flexible mask subtraction offers important improvements in quantitative estimates of the sizes of the arteries. In all the X-ray images used in the experiments, the precision of the method was at the 0.1-pixel level. The computation is extensive. However, some improvements can be achieved by doing the subtraction only at the coronary arteries and the immediate neighborhood instead of the whole image. The algorithm lends itself to implementation by parallel-structured hardware.     

Optimal temporal interpolation filter for motion-compensated frame rate up conversion.

Frame rate up conversion (FRUC) methods that employ motion have been proven to provide better image quality compared to nonmotion-based methods. While motion-based methods improve the quality of interpolation, artifacts are introduced in the presence of incorrect motion vectors. In this paper, we study the design problem of optimal temporal interpolation filter for motion-compensated FRUC (MC-FRUC). The optimal filter is obtained by minimizing the prediction error variance between the original frame and the interpolated frame. In FRUC applications, the original frame that is skipped is not available at the decoder, so models for the power spectral density of the original signal and prediction error are used to formulate the problem. The closed-form solution for the filter is obtained by Lagrange multipliers and statistical motion vector error modeling. The effect of motion vector errors on resulting optimal filters and prediction error is analyzed. The performance of the optimal filter is compared to nonadaptive temporal averaging filters by using two different motion vector reliability measures. The results confirm that to improve the quality of temporal interpolation in MC, the interpolation filter should be designed based on the reliability of motion vectors and the statistics of the MC prediction error.     

Spatial resolution properties of motion-compensated tomographic image reconstruction methods

Many motion-compensated image reconstruction (MCIR) methods have been proposed to correct for subject motion in medical imaging. MCIR methods incorporate motion models to improve image quality by reducing motion artifacts and noise. This paper analyzes the spatial resolution properties of MCIR methods and shows that nonrigid local motion can lead to nonuniform and anisotropic spatial resolution for conventional quadratic regularizers. This undesirable property is akin to the known effects of interactions between heteroscedastic log-likelihoods (e.g., Poisson likelihood) and quadratic regularizers. This effect may lead to quantification errors in small or narrow structures (such as small lesions or rings) of reconstructed images. This paper proposes novel spatial regularization design methods for three different MCIR methods that account for known nonrigid motion. We develop MCIR regularization designs that provide approximately uniform and isotropic spatial resolution and that match a user-specified target spatial resolution. Two-dimensional PET simulations demonstrate the performance and benefits of the proposed spatial regularization design methods.     

Motion estimation techniques for digital TV: a review and a newcontribution

The key to high performance in image sequence coding lies in an efficient reduction of the temporal redundancies. For this purpose, motion estimation and compensation techniques have been successfully applied. This paper studies motion estimation algorithms in the context of first generation coding techniques commonly used in digital TV. In this framework, estimating the motion in the scene is not an intrinsic goal. Motion estimation should indeed provide good temporal prediction and simultaneously require low overhead information. More specifically the aim is to minimize globally the bandwidth corresponding to both the prediction error information and the motion parameters. This paper first clarifies the notion of motion, reviews classical motion estimation techniques, and outlines new perspectives. Block matching techniques are shown to be the most appropriate in the framework of first generation coding. To overcome the drawbacks characteristic of most block matching techniques, this paper proposes a new locally adaptive multigrid block matching motion estimation technique. This algorithm has been designed taking into account the above aims. It leads to a robust motion field estimation precise prediction along moving edges and a decreased amount of side information in uniform areas. Furthermore, the algorithm controls the accuracy of the motion estimation procedure in order to optimally balance the amount of information corresponding to the prediction error and to the motion parameters. Experimental results show that the technique results in greatly enhanced visual quality and significant saving in terms of bit rate when compared to classical block matching techniques     

Suppression of slice selection axis motion artifacts in MRI

Patient motion during data acquisition in magnetic resonance imaging causes artifacts in the reconstructed image, which for two-dimensional Fourier transform imaging techniques appear as blurring and ghost repetitions of the moving structures. T. Mitsa et al. (1990) proposed a technique for suppressing artifacts from periodic motion along the slice selection axis. A different approach to the same problem is presented which is not restricted to periodic motion. The algorithm is verified using a simulated phantom and motion. It is also shown to perform well in the presence of noise and motion within the imaging plane.     

Volume-preserving nonrigid registration of MR breast images using free-form deformation with an incompressibility constraint

In this paper, we extend a previously reported intensity-based nonrigid registration algorithm by using a novel regularization term to constrain the deformation. Global motion is modeled by a rigid transformation while local motion is described by a free-form deformation based on B-splines. An information theoretic measure, normalized mutual information, is used as an intensity-based image similarity measure. Registration is performed by searching for the deformation that minimizes a cost function consisting of a weighted combination of the image similarity measure and a regularization term. The novel regularization term is a local volume-preservation (incompressibility) constraint, which is motivated by the assumption that soft tissue is incompressible for small deformations and short time periods. The incompressibility constraint is implemented by penalizing deviations of the Jacobian determinant of the deformation from unity. We apply the nonrigid registration algorithm with and without the incompressibility constraint to precontrast and post-contrast magnetic resonance (MR) breast images from 17 patients. Without using a constraint, the volume of contrast-enhancing lesions decreases by 1%-78% (mean 26%). Image improvement (motion artifact reduction) obtained using the new constraint is compared with that obtained using a smoothness constraint based on the bending energy of the coordinate grid by blinded visual assessment of maximum intensity projections of subtraction images. For both constraints, volume preservation improves, and motion artifact correction worsens, as the weight of the constraint penalty term increases. For a given volume change of the contrast-enhancing lesions (2% of the original volume), the incompressibility constraint reduces motion artifacts better than or equal to the smoothness constraint in 13 out of 17 cases (better in 9, equal in 4, worse in 4). The preliminary results suggest that incorporation of the incompressibility regularization term improves intensity-based free-form nonrigid registration of contrast-enhanced MR breast images by greatly reducing the problem of shrinkage of contrast-enhancing structures while simultaneously allowing motion artifacts to be substantially reduced.     

Bias field inconsistency correction of motion-scattered multislice MRI for improved 3D image reconstruction

A common solution to clinical MR imaging in the presence of large anatomical motion is to use fast multislice 2D studies to reduce slice acquisition time and provide clinically usable slice data. Recently, techniques have been developed which retrospectively correct large scale 3D motion between individual slices allowing the formation of a geometrically correct 3D volume from the multiple slice stacks. One challenge, however, in the final reconstruction process is the possibility of varying intensity bias in the slice data, typically due to the motion of the anatomy relative to imaging coils. As a result, slices which cover the same region of anatomy at different times may exhibit different sensitivity. This bias field inconsistency can induce artifacts in the final 3D reconstruction that can impact both clinical interpretation of key tissue boundaries and the automated analysis of the data. Here we describe a framework to estimate and correct the bias field inconsistency in each slice collectively across all motion corrupted image slices. Experiments using synthetic and clinical data show that the proposed method reduces intensity variability in tissues and improves the distinction between key tissue types.     

Visual data compression for multimedia applications

The compression of visual information in the framework of multimedia applications is discussed. To this end, major approaches to compress still as well as moving pictures are reviewed. The most important objective in any compression algorithm is that of compression efficiency. High-compression coding of still pictures can be split into three categories: waveform, second-generation, and fractal coding techniques. Each coding approach introduces a different artifact at the target bit rates. The primary objective of most ongoing research in this field is to mask these artifacts as much as possible to the human visual system. Video-compression techniques have to deal with data enriched by one more component, namely, the temporal coordinate. Either compression techniques developed for still images can be generalized for three-dimensional signals (space and time) or a hybrid approach can be defined based on motion compensation. The video compression techniques can then be classified into the following four classes: waveform, object-based, model-based, and fractal coding techniques. This paper provides the reader with a tutorial on major visual data-compression techniques and a list of references for further information as the details of each method     

Fourier domain techniques for digital angiography of the heart

Digital angiography is widely considered simply as a method in which images taken at different times are subtracted from each other. This paper presents some techniques which are performed in the frequency domain after the application of the Fourier transform. Nonselective bypass angiograms and intravenous ventriculograms are taken as examples to show that simple procedures utilizing these techniques exhibit the advantages of improved signal-to-noise ratio in the subtraction images, reduction of motion artifacts, easy application of phase-synchronous subtraction, integration, and quantitative visualization of blood propagation. It is furthermore shown that the storage of the angiographic image sequence as Fourier coefficients leads to data compression and convenient data access in an image database.     

System for Computing Montevideo Units for Monitoring Progress of Labor

Computation of uterine activity during labor from analog data is described. Uterine activity is measured in Montevideo units which are computed from the time record of intrauterine pressure. The objective of this paper is to describe the computation algorithms necessary to process an analog data record of intrauterine pressure to obtain Montevideo units. The analog data record contains unavoidable artifacts from patient motion, uterine elevation changes, plugged pressure tubes, and other extraneous sources. Algorithms are described that correct for artifacts, provide statistical smoothing, compute the first derivative, locate the maximum pressure of each contraction, and compute the intercontraction time interval.