Motion Blur Artifacts Research Articles

Digital staining facilitates biomedical microscopy.

Traditional staining of biological specimens for microscopic imaging entails time-consuming, laborious, and costly procedures, in addition to producing inconsistent labeling and causing irreversible sample damage. In recent years, computational "virtual" staining using deep learning techniques has evolved into a robust and comprehensive application for streamlining the staining process without typical histochemical staining-related drawbacks. Such virtual staining techniques can also be combined with neural networks designed to correct various microscopy aberrations, such as out-of-focus or motion blur artifacts, and improve upon diffracted-limited resolution. Here, we highlight how such methods lead to a host of new opportunities that can significantly improve both sample preparation and imaging in biomedical microscopy.

A Deblurring Method for Indirect Time-of-Flight Depth Sensor

Indirect time-of-flight (iToF) depth sensors can obtain depth from the phase offset between emitted and received modulated infrared (IR) pulsed light. However, these sensors suffer from motion blur artifacts when there are moving objects in the scene causing depth measurement distortion. By analyzing the mechanism of motion blur, this article summarizes motion blur into categories: half-frame motion blur and full-frame motion blur. For both blur categories, this article, respectively, proposes the deblurring method based on charge correction with the adjacent frame charge reference and the neighborhood similar charge reference. Based on the range resolution equation, the proposed pixel-based blur detection method can adaptively detect motion blur. For detected blurred pixels, the proposed deblurring method removes blur by recalculating the depth of blurred pixels. Motion blur was suppressed at 30 frames/s owing to the proposed deblurring method. The depth error is about 1.68% over the range of 1–2 m, with a modulation frequency of 40 MHz. Experimental results demonstrate that the proposed deblurring method can effectively eliminate the motion blur of moving objects with minimal computational cost.

Comparison of super-resolution and noise reduction for passive single-photon imaging

Single-photon sensitive image sensors have recently gained popularity in passive imaging applications where the goal is to capture photon flux (brightness) values of different scene points in the presence of challenging lighting conditions and scene motion. Recent work has shown that high-speed bursts of single-photon timestamp information captured using a single-photon avalanche diode camera can be used to estimate and correct for scene motion thereby improving signal-to-noise ratio and reducing motion blur artifacts. We perform a comparison of various design choices in the processing pipeline used for noise reduction, motion compensation, and upsampling of single-photon timestamp frames. We consider various pixelwise noise reduction techniques in combination with state-of-the-art deep neural network upscaling algorithms to super-resolve intensity images formed with single-photon timestamp data. We explore the trade space of motion blur and signal noise in various scenes with different motion content. Using real data captured with a hardware prototype, we achieved super-resolution reconstruction at frame rates up to 65.8 kHz (native sampling rate of the sensor) and captured videos of fast-moving objects. The best reconstruction is obtained with the motion compensation approach, which achieves a structural similarity (SSIM) of about 0.67 for fast-moving rigid objects. We are able to reconstruct subpixel resolution. These results show the relative superiority of our motion compensation compared to other approaches that do not exceed an SSIM of 0.5.

Automatic method for detecting specular reflection and motion blur artifacts on endoscopic images using complementary binary classifiers

Automatic method for detecting specular reflection and motion blur artifacts on endoscopic images using complementary binary classifiers

Isotropic quantitative differential phase contrast microscopy using radially asymmetric color-encoded pupil

Differential phase contrast (DPC) microscopy provides isotropic phase images by applying asymmetric illumination patterns on the sample. The movement of specimens during series image acquisition may lead to motion blur artifacts, which are difficult to prevent. Here, we propose a new method based on pupil engineering and color multiplexing to obtain an isotropic phase transfer function and to reduce the required frames simultaneously. Radially asymmetric color pupils are implemented in a DPC microscope using a programmable thin-film transistor as a digital pupil, which gives flexibility and dynamic control for projecting illumination patterns on samples. With our approach, an isotropic quantitative phase map can be obtained using only pairwise color images for phase reconstruction. A radially asymmetric color pupil is synthesized by encoding the red, green, and blue colors. To recover accurate phase values, a color-leakage correction algorithm is applied to calibrate each color channel. Compared to a half-circle illumination pupil, our method can significantly enhance the image acquisition speed. The phase recovery accuracy is more than 97%. To show the imaging performance of our proposed method, quantitative phase imaging of living 3T3 mouse fibroblast cells is performed. Our quantitative phase measurement method may find important applications in biomedical research.

SAR Analysis for Real Video Super- Resolution Improvement

This paper presents a simultaneous autoregressive (SAR) analysis method to describe the unknown signal-to-noise ratio (SNR) and the texture feature of low-quality real video frames when the ground-truth images are not available. The real video images degraded by the factors, such as electronic noise, oversaturated pixels, motion blur, and compression artifacts, often result in poor motion registration estimation, which makes the performance of the existing video super-resolution (VSR) algorithms lower than expected. It is hard to estimate the SNR of the low-quality real frames without any prior knowledge. To solve this problem, we made a connection between SAR hyperparameters and the SNR of real images. The relationship expression of them was given in this paper. Using the proposed method, well-registered low-quality real video frames can be selected to decrease the root mean squared error (RMSE) of motion estimation of video frames for VSR reconstruction improvement. The anomalous low-quality frame images whose SAR hyperparameters values are inconsistent with others will be considered for removal. Synthetic experiments were designed to illustrate how the SAR hyperparameters values vary with the variation of synthetic parameters. In order to better illustrate the effectiveness of the proposed method, real low-quality videos captured under different conditions were tested under VSR reconstruction experiments. The VSR reconstruction results show that the results obtained using SAR prior analysis have sharper edges and fewer ringing artifacts than the original results. It indicates that the proposed method is helpful to obtain better results of motion registration estimation for low-quality real video images.

Automated video-mosaicking approach for confocal microscopic imaging in vivo: an approach to address challenges in imaging living tissue and extend field of view

We describe a computer vision-based mosaicking method for in vivo videos of reflectance confocal microscopy (RCM). RCM is a microscopic imaging technique, which enables the users to rapidly examine tissue in vivo. Providing resolution at cellular-level morphology, RCM imaging combined with mosaicking has shown to be highly sensitive and specific for non-invasively guiding skin cancer diagnosis. However, current RCM mosaicking techniques with existing microscopes have been limited to two-dimensional sequences of individual still images, acquired in a highly controlled manner, and along a specific predefined raster path, covering a limited area. The recent advent of smaller handheld microscopes is enabling acquisition of videos, acquired in a relatively uncontrolled manner and along an ad-hoc arbitrarily free-form, non-rastered path. Mosaicking of video-images (video-mosaicking) is necessary to display large areas of tissue. Our video-mosaicking methods addresses this need. The method can handle unique challenges encountered during video capture such as motion blur artifacts due to rapid motion of the microscope over the imaged area, warping in frames due to changes in contact angle and varying resolution with depth. We present test examples of video-mosaics of melanoma and non-melanoma skin cancers, to demonstrate potential clinical utility.

Reducing Motion Blur Artifact of Foveal Projection for a Dynamic Focus-Plus-Context Display

This paper presents a novel technique to reduce the motion blur artifacts of foveal projection in a dynamic focus-plus-context (DF+C) display. The DF+C display is generally configured with multiple projectors and provides a nonuniform spatial resolution that consists of high-resolution (hi-res) regions (foveal projection) and low-resolution regions (peripheral projection). A serious problem of the DF+C display is motion blur, which inevitably occurs when a foveal projection is moved by a pan-tilt mirror or gantry. We propose a solution that reduces the motion blur artifacts, and evaluate how this solution improves the image quality using both qualitative and quantitative experiments. Our proposed method defines an error function to assess the displayed image quality as the difference between an original hi-res image and the displayed image by considering the nonuniform spatial property of human visual acuity. Then, it decides the set of positions and moving techniques of foveal projections so that the sum of errors during a video sequence is minimized. Through experiment, we confirmed that the proposed method can provide a better image quality and significantly improve the motion blur artifacts when compared with a conventional DF+C display.

A novel approach for enhancing very dark image sequences

We propose the methods, involving a denoiser and a tone mapper, that especially enhance very dark image sequences having an extremely small signal-to-noise ratio (SNR). The proposed denoiser and tone mapper, respectively, boosts the very small SNR and enlarges the extremely low dynamic range: they jointly form a conceptual framework to do the enhancement. We explicitly show that the conceptual framework adheres to a theoretical biological model of visual systems in nocturnal animals. The developed denoiser utilizes adaptive spatial–temporal filtering and effectively succeeds in reducing high noise and motion blur artifacts while preserving image details. The devised tone mapping method carries out different transformations for low and high intensity pixels by employing a single function which meanwhile takes into account the local contrast of each pixel, supporting a spatially and temporally consistent tone adjustment per frame. The conceptual framework and a customized post-process lead to a novel technique that is demonstrated to accomplish a satisfactory enhancement of very dark videos, and produce high quality restored image sequences – outperforming the recent state-of-the-art results. Finally, the proposed adaptive spatial–temporal filter and the post-processing procedure can be combined and adapted for normal-light image sequences to deal well with remarkably heavy noise.

Deformable left-ventricle mesh model for motion-compensated filtering in cardiac gated SPECT.

In this article, the authors present a motion-compensated spatiotemporal processing algorithm to reduce noise in cardiac gated SPECT. Cardiac gated SPECT data are particularly noisy because the acquired photon data are divided among a number of time frames (gates). Classical spatial reconstruction and processing techniques offer noise reduction but they are usually applied on each frame separately and fail to utilize temporal correlation between frames. In this work, the authors present a motion-compensated spatiotemporal postreconstruction filter offering noise reduction while minimizing motion-blur artifacts. The proposed method can be used regardless of the type of image-reconstruction method (analytical or iterative). The between-frame volumetric myocardium motion is estimated using a deformable mesh model based on the model of the myocardial surfaces. The estimated motion is then used to perform spatiotemporal filtering along the motion trajectories. Both the motion-estimation and spatiotemporal filtering methods seek to maintain the wall brightening seen during cardiac contraction. Wall brightening is caused by the partial volume effect, which is usually viewed as an artifact; however, wall brightening is a useful signature in clinical practice because it allows the clinician to visualize wall thickening. Therefore, the authors seek in their method to preserve the brightening effect. The authors find that the proposed method offers better noise reduction than several existing methods as quantitatively evaluated by signal-to-noise ratio, bias-variance plots, and ejection fraction analysis as well as on tested clinical data. The proposed method mitigates for noise in cardiac gated SPECT images using a postreconstruction motion-compensated filtering approach. Visual as well as quantitative evaluation show considerable improvement in image quality.

Image‐based iterative compensation of motion artifacts in computed tomography

This article presents an iterative method for compensation of motion artifacts for slowly rotating computed tomography (CT) systems. Patient's motion introduces inconsistencies among projections and yields severe reconstruction artifacts for free-breathing acquisitions. Streaks and doubling of structures can appear and the resolution is limited by strong blurring. The rationale of the proposed motion compensation method is to iteratively correct the reconstructed image by first decomposing the perceived motion in projection space, then reconstructing the motion artifacts in image space, and finally subtracting the artifacts from an initial image. The initial image is reconstructed from the acquired data and might contain motion blur artifacts but, nevertheless, is considered as a reference for estimating the reconstruction artifacts. Qualitative and quantitative figures are shown for experiments based on numerically simulated projections of a sequence of clinical images resulting from a respiratory-gated helical CT acquisition. The border of the diaphragm becomes progressively sharper and the contrast improves for small structures in the lungs. The originality of the technique stems from the fact that the patient motion is not explicitly estimated but the motion artifacts are reconstructed in image space. This approach could provide sharp static anatomical images on interventional C-arm systems or on slowly rotating X-ray equipments in radiotherapy.

P‐36: A Psychophysical Analysis on Perceptual Limitation of Motion Image Artifact Reduction Using 120Hz Displays

AbstractIn this paper, several psychophysical experiments are conducted to find the perceptual limitation in terms of duration, retinal velocity, and direction of motions. Moreover, the jerkiness artifacts are considered together with motion blur artifacts. The analysis results can provide important information for the computation reduction of frame rate up conversion.

Design and Development of a New Multi-Projection X-Ray System for Chest Imaging.

Overlapping anatomical structures may confound the detection of abnormal pathology, including lung nodules, in conventional single-projection chest radiography. To minimize this fundamental limiting factor, a dedicated digital multi-projection system for chest imaging was recently developed at the Radiology Department of Duke University. We are reporting the design of the multi-projection imaging system and its initial performance in an ongoing clinical trial. The system is capable of acquiring multiple full-field projections of the same patient along both the horizontal and vertical axes at variable speeds and acquisition frame rates. These images acquired in rapid succession from slightly different angles about the posterior-anterior (PA) orientation can be correlated to minimize the influence of overlying anatomy. The developed system has been tested for repeatability and motion blur artifacts to investigate its robustness for clinical trials. Excellent geometrical consistency was found in the tube motion, with positional errors for clinical settings within 1%. The effect of tube-motion on the image quality measured in terms of impact on the Modulation Transfer Function (MTF) was found to be minimal. The system was deemed clinic-ready and a clinical trial was subsequently launched. The flexibility of image acquisition built into the system provides a unique opportunity to easily modify it for different clinical applications, including tomosynthesis, correlation imaging (CI), and stereoscopic imaging.

SU‐DD‐A4‐02: Assessment of PET Estimated Tumor Volume by Four‐Dimensional Computed Tomography Measurements

Purpose: PET imaging is increasingly playing a key role in tumor detection, staging, and radiotherapy target definition of different cancer sites. However, the use of PET delineation in treatment planning of lung cancer is hindered by breathing motion blur artifacts. These artifacts complicate the evaluation of segmentation algorithms accuracy for PET. In this work, we propose to assess PET estimated tumor volume by comparing with four‐dimensional computed tomography measurements under different motion conditions. Method and Materials: We analyzed six NSCLC patients' datasets who underwent pre‐treatment FDG‐PET/CT scanning and 4D‐CT simulations. The 4D‐CT were acquired according to a ciné‐mode 4D protocol; 25 scans were collected at each couch position while the patient underwent simultaneous bellows and spirometry measurements. Volumetric datasets were rebinned to the following phases: end‐of exhalation, beginning‐of‐exhalation, mid‐exhalation, mid‐inhalation, and end‐of‐inhalation, in addition to computing average CT and MIP datasets. The tumor volume was manually contoured on nine PET and CT datasets by two physicians for each patient to provide a gold standard for comparison. Different PET segmentation algorithms based on SUV thresholding and active contours were evaluated. Motion artifacts were mitigated using a deconvolution‐based deblurring approach. Results: Our preliminary analysis indicates that manual contouring on 4D‐CT datasets produced relatively consistent results across the different phases by the two observers. Motion deblurring had varying effects on the evaluated algorithms; it increased the estimated volume by the 40% maximum SUV thresholding and reduced it when applying an SUV cutoff of 2.5. The active contour model produced robust results independent of the blurring effect. Conclusion: We have investigated a 4DCT approach for assessing the performance of PET segmentation methods in lung cancer. Our preliminary results indicate high sensitivity of thresholding methods and better robustness by active contouring models. Further investigation is required to improve accuracy relative to the 4DCT gold standard.

10.2: Motion Artifacts in Medical Applications

AbstractIn this paper an overdrive compensation for motion blur artifacts of LCD displays is described. It fits to those medical applications where moving images for diagnostic purposes are presented. This compensation relies on characterization of the LCD‐display‐transitions with a full‐state‐model, DICOM‐grayscale mapping for nonlinear compensation of the human vision and overdrive on multiple frames. Transitions that cannot complete in one frame are overdriven in subsequent frames with accurate computation of levels.

51.1: Temporal Impulse Response and Bandwidth of Displays in Relation to Motion Blur

Displays with non-impulse temporal response suffer from motion blur artifacts. This paper will show that the temporal impulse response and related temporal bandwidth provide a simple and direct way to quantify motion blur.

Saving Bits—The Impact of MCTF Enhanced Noise Reduction

Motion Compensated Temporal Filtering (MCTF) has made a significant impact on the DTV industry by saving bits and allowing operators to deliver consistently higher quality video at lower rates than ever before. All content tends to contain some noise, the random and unwanted portion of a signal. Even modern facilities equipped with the latest digital production equipment will inevitably import noisy content unless the incoming material is thoroughly cleaned. Noise reduction (NR) and filtering can substantially improve the video received by a viewer if the right techniques are applied to remove noise prior to compression. Selectively removing noise is a challenge because it shares space with valuable picture detail. An ideal noise reduction process will allow powerful suppression of random noise while preserving clean video content. The major advantage of MCTF is its inherent ability to remove noise without introducing motion blur artifacts. This technique has been known for many years, but the costs associated with a high processing overhead has kept it from being commercially viable. This paper will explain how integration of the new MCTF technique with existing technologies has dramatically improved video-compression efficiency.