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Journal articles on the topic 'Motion blur'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Gosselin, Frédéric, and Claude Lamontagne. "Motion-Blur Illusions." Perception 26, no. 7 (1997): 847–55. http://dx.doi.org/10.1068/p260847.

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The still-radii illusion, the figure-of-eight illusion, the band-of-heightened-intensity illusion and the dark-blurred-concentric-circles illusion have remained, until now, isolated relatively ill-explained phenomena. A single algorithmic model is proposed which explains these four visual illusions. In fact, this model predicts phenomena produced by motion of any gray-shaded patterns relative to the eyes (termed ‘motion-blur illusions’). Results of a computer simulation of the model are presented. A novel instance of the proposed class of illusions, which can be readily experienced by the read

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2

Askari Javaran, Taiebeh, and Hamid Hassanpour. "Using a Blur Metric to Estimate Linear Motion Blur Parameters." Computational and Mathematical Methods in Medicine 2021 (October 28, 2021): 1–8. http://dx.doi.org/10.1155/2021/6048137.

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Motion blur is a common artifact in image processing, specifically in e-health services, which is caused by the motion of a camera or scene. In linear motion cases, the blur kernel, i.e., the function that simulates the linear motion blur process, depends on the length and direction of blur, called linear motion blur parameters. The estimation of blur parameters is a vital and sensitive stage in the process of reconstructing a sharp version of a motion blurred image, i.e., image deblurring. The estimation of blur parameters can also be used in e-health services. Since medical images may be blu

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Watson, Andrew B., and Albert J. Ahumada. "14.2: Visible Motion Blur: A Perceptual Metric for Display Motion Blur." SID Symposium Digest of Technical Papers 41, no. 1 (2010): 184. http://dx.doi.org/10.1889/1.3500365.

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4

Georgeson, Mark A., and Stephen T. Hammett. "Seeing blur: ‘motion sharpenin’ without motion." Proceedings of the Royal Society of London. Series B: Biological Sciences 269, no. 1499 (2002): 1429–34. http://dx.doi.org/10.1098/rspb.2002.2029.

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Oktay, Tugrul, Harun Celik, and Ilke Turkmen. "Constrained control of helicopter vibration to reduce motion blur." Aircraft Engineering and Aerospace Technology 90, no. 9 (2018): 1326–36. http://dx.doi.org/10.1108/aeat-02-2017-0068.

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Purpose The purpose of this paper is to examine the success of constrained control on reducing motion blur which occurs as a result of helicopter vibration. Design/methodology/approach Constrained controllers are designed to reduce the motion blur on images taken by helicopter. Helicopter vibrations under tight and soft constrained controllers are modeled and added to images to show the performance of controllers on reducing blur. Findings The blur caused by vibration can be reduced via constrained control of helicopter. Research limitations/implications The motion of camera is modeled and ass

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Oberberger, Max, Matthäus G. Chajdas, and Rüdiger Westermann. "Spatiotemporal Variance-Guided Filtering for Motion Blur." Proceedings of the ACM on Computer Graphics and Interactive Techniques 5, no. 3 (2022): 1–13. http://dx.doi.org/10.1145/3543871.

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Adding motion blur to a scene can help to convey the feeling of speed even at low frame rates. Monte Carlo ray tracing can compute accurate motion blur, but requires a large number of samples per pixel to converge. In comparison, rasterization, in combination with a post-processing filter, can generate fast, but not accurate motion blur from a single sample per pixel. We build upon a recent path tracing denoiser and propose its variant to simulate ray-traced motion blur, enabling fast and high-quality motion blur from a single sample per pixel. Our approach creates temporally coherent renderin

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Shi, Lixiang, and Jianping Tan. "Discovery, Quantitative Recurrence, and Inhibition of Motion-Blur Hysteresis Phenomenon in Visual Tracking Displacement Detection." Sensors 23, no. 19 (2023): 8024. http://dx.doi.org/10.3390/s23198024.

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Motion blur is common in video tracking and detection, and severe motion blur can lead to failure in tracking and detection. In this work, a motion-blur hysteresis phenomenon (MBHP) was discovered, which has an impact on tracking and detection accuracy as well as image annotation. In order to accurately quantify MBHP, this paper proposes a motion-blur dataset construction method based on a motion-blur operator (MBO) generation method and self-similar object images, and designs APSF, a MBO generation method. The optimized sub-pixel estimation method of the point spread function (SPEPSF) is used

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Makkad, Satwinderpal S. "Range from motion blur." Optical Engineering 32, no. 8 (1993): 1915. http://dx.doi.org/10.1117/12.143301.

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9

Dongming, Li, Su Zhengbo, Su Wei, and Zhang Lijuan. "Research on Cross-Correlative Blur Length Estimation Algorithm in Motion Blur Image." Journal of Advanced Computational Intelligence and Intelligent Informatics 20, no. 1 (2016): 155–62. http://dx.doi.org/10.20965/jaciii.2016.p0155.

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This paper proposes a motion blur length estimation method that is applied to motion blur image restoration. This method applies a cross-correlation algorithm to multi-frame motion-degraded images. In order to find the motion blur parameters, the Radon transform method is used to estimate the motion blur angle. We extract the gray value of pixels around the blur center, calculate the correlation for obtaining motion blur length, and use the Lucy-Richardson iterative algorithm to restore the degraded image. Experiment results show that this method can accurately estimate blur parameters, reduce

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Li, Haoying, Ziran Zhang, Tingting Jiang, Peng Luo, Huajun Feng, and Zhihai Xu. "Real-World Deep Local Motion Deblurring." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 1 (2023): 1314–22. http://dx.doi.org/10.1609/aaai.v37i1.25215.

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Most existing deblurring methods focus on removing global blur caused by camera shake, while they cannot well handle local blur caused by object movements. To fill the vacancy of local deblurring in real scenes, we establish the first real local motion blur dataset (ReLoBlur), which is captured by a synchronized beam-splitting photographing system and corrected by a post-progressing pipeline. Based on ReLoBlur, we propose a Local Blur-Aware Gated network (LBAG) and several local blur-aware techniques to bridge the gap between global and local deblurring: 1) a blur detection approach based on b

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Rodriguez, Bryan, Xinxiang Zhang, and Dinesh Rajan. "Probabilistic Modeling of Motion Blur for Time-of-Flight Sensors." Sensors 22, no. 3 (2022): 1182. http://dx.doi.org/10.3390/s22031182.

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Synthetically creating motion blur in two-dimensional (2D) images is a well-understood process and has been used in image processing for developing deblurring systems. There are no well-established techniques for synthetically generating arbitrary motion blur within three-dimensional (3D) images, such as depth maps and point clouds since their behavior is not as well understood. As a prerequisite, we have previously developed a method for generating synthetic motion blur in a plane that is parallel to the sensor detector plane. In this work, as a major extension, we generalize our previously d

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Zhao, Xuesen, Xianping Zhang, Wei Zhao, Jin Xu, Hongyu Wang, and Wonjun Song. "62‐3: The Effect of OLED Device Capacitance on Low Gray Levels Motion Blur." SID Symposium Digest of Technical Papers 55, S1 (2024): 539–41. http://dx.doi.org/10.1002/sdtp.17133.

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In this study, we have demonstrated the phenomenon of motion blur in mobile phone during the process of application and analyzed the relevant factors that affect motion blur at low gray levels, including thin film transistor (TFT), organic light emitting diodes (OLED), and electronic code. Our findings indicated that the OLED capacitance has a more significant impact on phenomenon of motion blur than TFT and electronic code. Furthermore, we discovered that OLED capacitance is inversely proportional to the brightness of the first frame when switching from a black screen to white/red/green/blue

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Lee, Donghyun, Hyeoksu Kwon, and Kyoungsu Oh. "Real-Time Motion Blur Using Multi-Layer Motion Vectors." Applied Sciences 14, no. 11 (2024): 4626. http://dx.doi.org/10.3390/app14114626.

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Traditional methods for motion blur, often relying on a single layer, deviate from the correct colors. We propose a multilayer rendering method that closely approximates the motion blur effect. Our approach stores motion vectors for each pixel, divides these vectors into multiple sample points, and performs a backward search from the current pixel. The color at a sample point is sampled if it shares the same motion vector as its origin. This procedure repeats across layers, with only the nearest color values sampled for depth testing. The average color sampled at each point becomes that of the

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Son, Hyeongseok, Junyong Lee, Jonghyeop Lee, Sunghyun Cho, and Seungyong Lee. "Recurrent Video Deblurring with Blur-Invariant Motion Estimation and Pixel Volumes." ACM Transactions on Graphics 40, no. 5 (2021): 1–18. http://dx.doi.org/10.1145/3453720.

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For the success of video deblurring, it is essential to utilize information from neighboring frames. Most state-of-the-art video deblurring methods adopt motion compensation between video frames to aggregate information from multiple frames that can help deblur a target frame. However, the motion compensation methods adopted by previous deblurring methods are not blur-invariant, and consequently, their accuracy is limited for blurry frames with different blur amounts. To alleviate this problem, we propose two novel approaches to deblur videos by effectively aggregating information from multipl

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15

Luo, Jinhui, and Tao Bo. "Research on Fast Estimation Method of Fuzzy Parameters for Motion Blurred Images." Journal of Physics: Conference Series 2029, no. 1 (2021): 012111. http://dx.doi.org/10.1088/1742-6596/2029/1/012111.

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Abstract Motion blur distortion is the most common type of image distortion in daily life. the research on motion-blurred image restoration technology has developed more mature. Classical algorithms such as Wiener filter and Kalman filter and various improved algorithms can achieve better results, but they take a long time and have great limitations in actual image restoration application scenarios. To solve this problem, this paper proposes an algorithm for fast restoration of image motion blur, an improved algorithm based on Randon transform to judge the image motion blur angle, and studies

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Argaw, Dawit Mureja, Junsik Kim, Francois Rameau, Jae Won Cho, and In So Kweon. "Optical Flow Estimation from a Single Motion-blurred Image." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (2021): 891–900. http://dx.doi.org/10.1609/aaai.v35i2.16172.

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In most of computer vision applications, motion blur is regarded as an undesirable artifact. However, it has been shown that motion blur in an image may have practical interests in fundamental computer vision problems. In this work, we propose a novel framework to estimate optical flow from a single motion-blurred image in an end-to-end manner. We design our network with transformer networks to learn globally and locally varying motions from encoded features of a motion-blurred input, and decode left and right frame features without explicit frame supervision. A flow estimator network is then

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17

HAN Xiao-fang, 韩小芳, and 胡家升 HU Jia-sheng. "Restoration of Motion Blur Image and Defocus Blur Image." ACTA PHOTONICA SINICA 41, no. 1 (2012): 87–93. http://dx.doi.org/10.3788/gzxb20124101.0087.

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18

Boracchi, Giacomo, and Alessandro Foi. "Uniform Motion Blur in Poissonian Noise: Blur/Noise Tradeoff." IEEE Transactions on Image Processing 20, no. 2 (2011): 592–98. http://dx.doi.org/10.1109/tip.2010.2062196.

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Tiwari, Shamik, V. P. Shukla, S. R. Biradar, and A. K. Singh. "Blur parameters identification for simultaneous defocus and motion blur." CSI Transactions on ICT 2, no. 1 (2014): 11–22. http://dx.doi.org/10.1007/s40012-014-0039-3.

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20

Abotula, Dileep Kumar, and Bodasingi Nalini. "Estimation and correction of motion blur in digital images." i-manager’s Journal on Image Processing 9, no. 4 (2022): 1. http://dx.doi.org/10.26634/jip.9.4.19285.

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Digital images play a very important role in developing computer-aided systems. The motion blur and blur in such types of images affect the accuracy of the system. Therefore, it is a challenging task to estimate and remove the blur in the images. In the present paper, an attempt is made to use a Convolutional Neural Network (CNN) model to estimate and remove the blur in the images. The CNN model with different functions helps to improve the accuracy of removing blur from the images. Different network functions, such as ReLU and Sigmoid, and their combinations are analyzed for the modeling of C

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Wang, Shiqiang, Shijie Zhang, Mingfeng Ning, and Botian Zhou. "Motion Blurred Star Image Restoration Based on MEMS Gyroscope Aid and Blur Kernel Correction." Sensors 18, no. 8 (2018): 2662. http://dx.doi.org/10.3390/s18082662.

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Under dynamic conditions, motion blur is introduced to star images obtained by a star sensor. Motion blur affects the accuracy of the star centroid extraction and the identification of stars, further reducing the performance of the star sensor. In this paper, a star image restoration algorithm is investigated to reduce the effect of motion blur on the star image. The algorithm includes a blur kernel calculation aided by a MEMS gyroscope, blur kernel correction based on the structure of the star strip, and a star image reconstruction method based on scaled gradient projection (SGP). Firstly, th

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Li, Qi Shen, and Jian Gong Chen. "PSF Estimation and Image Restoration for Motion Blurred Images." Advanced Materials Research 562-564 (August 2012): 2124–27. http://dx.doi.org/10.4028/www.scientific.net/amr.562-564.2124.

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Point spread function (PSF) estimation and image restoration algorithm are the hotspots In the research of motion blurred image restoration. In order to improve the efficacy of image restoration, an improved algorithm named quadric transforms (QT) method is proposed in this paper by analyzing the restoration process of motion blurred images. Firstly, Fourier transform and homomorphism transform are applied to the original motion blurred image, and then the Fourier transform and homomorphism transform are used again to the obtained spectrum image. Secondly, the motion blur direction is estimate

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23

Ma, Bo, Lianghua Huang, Jianbing Shen, Ling Shao, Ming-Hsuan Yang, and Fatih Porikli. "Visual Tracking Under Motion Blur." IEEE Transactions on Image Processing 25, no. 12 (2016): 5867–76. http://dx.doi.org/10.1109/tip.2016.2615812.

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24

Pulujkar, Mosami P., and Shaila D. Apte. "Demosaicking Images with Motion Blur." Journal of Medical Imaging and Health Informatics 2, no. 4 (2012): 373–77. http://dx.doi.org/10.1166/jmihi.2012.1111.

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Pulujkar, MosamiP, and ShailaD Apte. "Demosaicking Images with Motion Blur." Journal of Medical Imaging and Health Informatics 3, no. 1 (2013): 17–21. http://dx.doi.org/10.1166/jmihi.2013.1128.

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26

Wloka, Matthias M., and Robert C. Zeleznik. "Interactive real-time motion blur." Visual Computer 12, no. 6 (1996): 283–95. http://dx.doi.org/10.1007/s003710050065.

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Agrawal, Amit, Yi Xu, and Ramesh Raskar. "Invertible motion blur in video." ACM Transactions on Graphics 28, no. 3 (2009): 1–8. http://dx.doi.org/10.1145/1531326.1531401.

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Tani, Jacopo, Sandipan Mishra, and John T. Wen. "Motion Blur-Based State Estimation." IEEE Transactions on Control Systems Technology 24, no. 3 (2016): 1012–19. http://dx.doi.org/10.1109/tcst.2015.2473004.

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Wloka, Matthias M., and Robert C. Zeleznik. "Interactive real-time motion blur." Visual Computer 12, no. 6 (1996): 283–95. http://dx.doi.org/10.1007/bf01782290.

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Hong, MinhPhuoc, Jinhyung Choi, and Kyoungsu Oh. "Real-Time Motion Blur using Approximated Motion Trails." Journal of Korea Game Society 17, no. 1 (2017): 17–26. http://dx.doi.org/10.7583/jkgs.2017.17.1.17.

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Jha, Tantra Nath. "Velocity Detection from a Motion Blur Image Using Radon Transformation." Tribhuvan University Journal 32, no. 2 (2018): 243–48. http://dx.doi.org/10.3126/tuj.v32i2.24721.

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Motion blur is the result when the camera shutter remains open for an extended period of time and a relative motion between camera and object occurs. An approach for velocity detection based on motion blurred images has been implemented by the Radon transformation. The motion blur parameters are first estimated from the acquired images by using Radon transformation and then used to detect the speed of the moving object in the scene. Here established a link between the motion blur information of a 2D image and camera manufacturer’s data sheet and its calibration

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Chang, Chia-Feng, Jiunn-Lin Wu, and Ting-Yu Tsai. "A Single Image Deblurring Algorithm for Nonuniform Motion Blur Using Uniform Defocus Map Estimation." Mathematical Problems in Engineering 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/6089650.

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One of the most common artifacts in digital photography is motion blur. When capturing an image under dim light by using a handheld camera, the tendency of the photographer’s hand to shake causes the image to blur. In response to this problem, image deblurring has become an active topic in computational photography and image processing in recent years. From the view of signal processing, image deblurring can be reduced to a deconvolution problem if the kernel function of the motion blur is assumed to be shift invariant. However, the kernel function is not always shift invariant in real cases;

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Vimal, Vrince. "Mixture of Gaussian Blur Kernel Representation for Blind Image Restoration." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 10, no. 1 (2019): 589–95. http://dx.doi.org/10.17762/turcomat.v10i1.13553.

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The use of blind image restoration, sharpness of edges may frequently be restored using previous information from a picture. De-blurring is the technique of taking out blurring flaws of the steady photographs, including motion or defocus aberration-related blur. the appearance of fast-moving the appearance of fast-moving entities flashing in still images flashing in a still photograph is known as motion blur. When an image is blurred using a Gaussian function, the result is a Gaussian blur. The employment of different sparse priors, either for the implicit photos or the motion blur kernels, co

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Dohr, S., M. Muick, B. Schachinger, and M. Gruber. "IMAGE MOTION COMPENSATION – THE VEXCEL APPROACH." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B1-2022 (May 30, 2022): 333–38. http://dx.doi.org/10.5194/isprs-archives-xliii-b1-2022-333-2022.

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Abstract. Motion compensation in general and forward motion compensation in particular was an important milestone in aerial imaging when presented for film-based camera systems in the late 90ts of the last century. It focused on the forward motion compensation to enhance the image quality when flight speed and image scale produce such motion blur even at short exposure time. Another development and milestone in aerial photogrammetry, the active mount, contributed as well to reduce motion blur.When digital aerial cameras replaced the film-based camera systems in the first decade of the 21st cen

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Arslan, Ahmet, Gokhan Koray Gultekin, and Afsar Saranli. "IMU-aided adaptive mesh-grid based video motion deblurring." PeerJ Computer Science 10 (November 25, 2024): e2540. http://dx.doi.org/10.7717/peerj-cs.2540.

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Motion blur is a problem that degrades the visual quality of images for human perception and also challenges computer vision tasks. While existing studies mostly focus on deblurring algorithms to remove uniform blur due to their computational efficiency, such approaches fail when faced with non-uniform blur. In this study, we propose a novel algorithm for motion deblurring that utilizes an adaptive mesh-grid approach to manage non-uniform motion blur with a focus on reducing the computational cost. The proposed method divides the image into a mesh-grid and estimates the blur point spread funct

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Lee, Eunsung, Eunjung Chae, Hejin Cheong, and Joonki Paik. "Fast Motion Deblurring Using Sensor-Aided Motion Trajectory Estimation." Scientific World Journal 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/649272.

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This paper presents an image deblurring algorithm to remove motion blur using analysis of motion trajectories and local statistics based on inertial sensors. The proposed method estimates a point-spread-function (PSF) of motion blur by accumulating reweighted projections of the trajectory. A motion blurred image is then adaptively restored using the estimated PSF and spatially varying activity map to reduce both restoration artifacts and noise amplification. Experimental results demonstrate that the proposed method outperforms existing PSF estimation-based motion deconvolution methods in the s

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Liu, Shanshan, Qingbin Huang, and Minghui Wang. "Multi-Frame Blind Super-Resolution Based on Joint Motion Estimation and Blur Kernel Estimation." Applied Sciences 12, no. 20 (2022): 10606. http://dx.doi.org/10.3390/app122010606.

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Multi-frame super-resolution makes up for the deficiency of sensor hardware and significantly improves image resolution by using the information of inter-frame and intra-frame images. Inaccurate blur kernel estimation will enlarge the distortion of the estimated high-resolution image. Therefore, multi-frame blind super resolution with unknown blur kernel is more challenging. For the purpose of reducing the impact of inaccurate motion estimation and blur kernel estimation on the super-resolved image, we propose a novel method combining motion estimation, blur kernel estimation and super resolut

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Kashani, Hany, Graham Wright, Ali Ursani, Garry Liu, Masoud Hashemi, and Narinder Paul. "Restricting motion effects in CT coronary angiography." British Journal of Radiology 92, no. 1103 (2019): 20190384. http://dx.doi.org/10.1259/bjr.20190384.

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Objective: Evaluation of coronary CT image blur using multi segment reconstruction algorithm. Methods: Cardiac motion was simulated in a Catphan. CT coronary angiography was performed using 320 × 0.5 mm detector array and 275 ms gantry rotation. 1, 2 and 3 segment reconstruction algorithm, three heart rates (60, 80 and 100bpm), two peak displacements (4, 8 mm) and three cardiac phases (55, 35, 75%) were used. Wilcoxon test compared image blur from the different reconstruction algorithms. Results: Image blur for 1, 2 and 3 segments in: 60 bpm, 75% R–R interval and 8 mm peak displacement: 0.714,

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Lin, Songnan, Jiawei Zhang, Jinshan Pan, et al. "Learning to Deblur Face Images via Sketch Synthesis." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 11523–30. http://dx.doi.org/10.1609/aaai.v34i07.6818.

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The success of existing face deblurring methods based on deep neural networks is mainly due to the large model capacity. Few algorithms have been specially designed according to the domain knowledge of face images and the physical properties of the deblurring process. In this paper, we propose an effective face deblurring algorithm based on deep convolutional neural networks (CNNs). Motivated by the conventional deblurring process which usually involves the motion blur estimation and the latent clear image restoration, the proposed algorithm first estimates motion blur by a deep CNN and then r

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Oktay, Tugrul, Harun Celik, and Ilke Turkmen. "Maximizing autonomous performance of fixed-wing unmanned aerial vehicle to reduce motion blur in taken images." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 232, no. 7 (2018): 857–68. http://dx.doi.org/10.1177/0959651818765027.

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In this study, reducing motion blur in images taken by our unmanned aerial vehicle is investigated. Since shakes of unmanned aerial vehicle cause motion blur in taken images, autonomous performance of our unmanned aerial vehicle is maximized to prevent it from shakes. In order to maximize autonomous performance of unmanned aerial vehicle (i.e. to reduce motion blur), initially, camera mounted unmanned aerial vehicle dynamics are obtained. Then, optimum location of unmanned aerial vehicle camera is estimated by considering unmanned aerial vehicle dynamics and autopilot parameters. After improvi

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Kang, Ruidan, Jiajin Li, Xiaojun Teng, Boyan Lv, and Cangzhi Wu. "P‐45: An Evaluation Method of Moving Picture Response Time for Organic Light‐Emitting Diode Motion Blur." SID Symposium Digest of Technical Papers 54, no. 1 (2023): 1682–84. http://dx.doi.org/10.1002/sdtp.16922.

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The motion image quality of organic light‐emitting diode (OLED) degrades by motion blur due to the hold‐type display. Moving picture response time (MPRT) is a representative index to evaluate the performance of the moving image. A method is proposed to evaluate the motion blur of OLED display.

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Hayashi, Toshiyuki, and Takashi Tsubouchi. "Estimation and Sharpening of Blur in Degraded Images Captured by a Camera on a Moving Object." Sensors 22, no. 4 (2022): 1635. http://dx.doi.org/10.3390/s22041635.

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In this research, we aim to propose an image sharpening method to make it easy to identify concrete cracks from blurred images captured by a moving camera. This study is expected to help realize social infrastructure maintenance using a wide range of robotic technologies, and to solve the future labor shortage and shortage of engineers. In this paper, a method to estimate parameters of motion blur for Point Spread Function (PSF) is mainly discussed, where we assume that there are two main degradation factors caused by the camera, out-of-focus blur and motion blur. A major contribution of this

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Kwon, Hyeok-su, Donghyun Lee, and Kyoungsu Oh. "Real-Time Motion Blur using Multi–layer Motion vector." Journal of Korea Game Society 23, no. 4 (2023): 93–101. http://dx.doi.org/10.7583/jkgs.2023.23.4.93.

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Zhuang, Hong. "Enhanced DeblurGAN: An advanced combinatorial model for motion blur removal in low-light photography." Applied and Computational Engineering 51, no. 1 (2024): 20–25. http://dx.doi.org/10.54254/2755-2721/51/20241152.

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This article aims to address the challenge of eliminating low-light motion blur, a problem that lacks effective solutions, despite being crucial in various application scenarios. For instance, it can help in the identification of moving individuals or license plates during nocturnal surveillance, filming running videos after dark, and managing animals in rural areas at night. These examples represent commonplace and significant scenarios. These are all important domains, but few approaches are effective at handling such specific cases simultaneously. This paper utilizes a fusion model to incre

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Tabellion, Eric, Nikhil Karnad, Noa Glaser, Ben Weiss, David E. Jacobs, and Yael Pritch. "Computational Long Exposure Mobile Photography." ACM Transactions on Graphics 42, no. 4 (2023): 1–15. http://dx.doi.org/10.1145/3592124.

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Long exposure photography produces stunning imagery, representing moving elements in a scene with motion-blur. It is generally employed in two modalities, producing either a foreground or a background blur effect. Foreground blur images are traditionally captured on a tripod-mounted camera and portray blurred moving foreground elements, such as silky water or light trails, over a perfectly sharp background landscape. Background blur images, also called panning photography, are captured while the camera is tracking a moving subject, to produce an image of a sharp subject over a background blurr

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Tomionko, Joseph, Moussa Magara Traoré, and Drissa Traoré. "Blur and Motion Blur Influence on Recognition Performance of Color Face." WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS 19 (November 17, 2022): 272–76. http://dx.doi.org/10.37394/23209.2022.19.28.

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Face recognition is an existing and one of the most prominent biometrics techniques, including the processing of images. It is widely used in many applications. The performance of such systems is directly due to face image quality. Since blur and motion blur are common imagery problems, this paper explores the influence of such disturbances on color face recognition performance. The research described in this paper compares the performance of the face recognition algorithm based on the Haar features and Local Binary Patterns Histograms when it uses color face images of good quality, images wit

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Nagiub, Mena, Thorsten Beuth, Ganesh Sistu, Heinrich Gotzig, and Ciarán Eising. "Depth Prediction Improvement for Near-Field iToF Lidar in Low-Speed Motion State." Sensors 24, no. 24 (2024): 8020. https://doi.org/10.3390/s24248020.

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Current deep learning-based phase unwrapping techniques for iToF Lidar sensors focus mainly on static indoor scenarios, ignoring motion blur in dynamic outdoor scenarios. Our paper proposes a two-stage semi-supervised method to unwrap ambiguous depth maps affected by motion blur in dynamic outdoor scenes. The method trains on static datasets to learn unwrapped depth map prediction and then adapts to dynamic datasets using continuous learning methods. Additionally, blind deconvolution is introduced to mitigate the blur. The combined use of these methods produces high-quality depth maps with red

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Yang, ChangMo, Kyongho Lim, and Tae-Yong Park. "P‐39: Simulation of Perceived Motion Blur on 480Hz OLED Monitor." SID Symposium Digest of Technical Papers 55, no. 1 (2024): 1519–22. http://dx.doi.org/10.1002/sdtp.17843.

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Refresh rate is an important specification for gaming OLED monitors. With the development of the gaming industry and the graphics processing unit (GPU), the demand for gaming monitors that supporting high refresh rates is increasing. In this paper, simulation methods for perceived motion blur are proposed to predict the degree of blur according to the refresh rate. Experimental results indicate that the proposed simulation methods are quite effective in predicting the degree of motion blur. In addition, this paper presents the predicted results of blur as the refresh rate increases up to 480Hz

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Rønnow, Mads J. L., Ulf Assarsson, and Marco Fratarcangeli. "Fast analytical motion blur with transparency." Computers & Graphics 95 (April 2021): 36–46. http://dx.doi.org/10.1016/j.cag.2021.01.006.

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Lin, Huei-Yung. "Depth from motion and defocus blur." Optical Engineering 45, no. 12 (2006): 127201. http://dx.doi.org/10.1117/1.2403851.

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