scholarly journals Fast Motion Deblurring Using Sensor-Aided Motion Trajectory Estimation

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Eunsung Lee ◽  
Eunjung Chae ◽  
Hejin Cheong ◽  
Joonki Paik
Keyword(s):  
Inertial Sensors ◽  
Motion Blur ◽  
Trajectory Estimation ◽  
Motion Trajectories ◽  
Motion Deblurring ◽  
Spread Function ◽  
Blurred Image ◽  
Fast Motion ◽  
Noise Amplification ◽  
Spatially Varying

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 sense of both objective and subjective performance measures. The proposed algorithm can be employed in various imaging devices because of its efficient implementation without an iterative computational structure.

A Research on the Optimization of Fuzzy Image

2013 ◽  
Vol 409-410 ◽  
pp. 1593-1596
Author(s):  
Xue Feng Wu ◽  
Yu Fan
Keyword(s):  
Mathematical Model ◽  
Image Restoration ◽  
Point Spread Function ◽  
Motion Blur ◽  
Inverse Filtering ◽  
Point Spread ◽  
Spread Function ◽  
Blurred Image ◽  
Blurred Images ◽  
Fuzzy Image

The restore algorithm of the image blurred by motion is proposed, and a mathematical model based on motion blur system is eomtrueted£®The Point spread function of the motion blur is given According to the characteristics of blurred images the parameters of point spread function are estimated ,and three methods are introduced for image restoration. The three methods are inverse filtering of image restoration, Lucy-Richardson image restoration and Wiener image restoration. The principles of the three image restoration methods are analyzed. The motion blurred image restoration experiment is made. The results show that the visibility of the image is improved, and the image restoration is more stable.

The Restoration of Motion Blurred Images Based on the Background Modeling

2014 ◽  
Vol 687-691 ◽  
pp. 3591-3595
Author(s):  
Jiang Yang Chen ◽  
Xi Ling Luo
Keyword(s):  
Wiener Filter ◽  
Background Modeling ◽  
Motion Blur ◽  
Edge Density ◽  
Image Generation ◽  
Point Spread ◽  
Spread Function ◽  
Blurred Image ◽  
Image Area ◽  
Blurred Images

For the mutual effects of camera shake and subject movement, the image generation space varying motion blur. In order to achieve image restoration, firstly dividing the image area using the Gaussian background modeling, and updated model adaptive to improve the speed and convergence accuracy. Then use the total variation (TV) of the L1 model to estimate the regional point spread function (PSF), and adopted the edge density weight to reduce small edge’s interference for the PSF estimates. Eventually to restored image by Wiener filter. Through experimental analysis, compared with other algorithms, our algorithms get better results in the space varies motion-blurred image.

scholarly journals Region-Adaptive Dense Network for Efficient Motion Deblurring

2020 ◽  
Vol 34 (07) ◽  
pp. 11882-11889 ◽  
Author(s):  
Kuldeep Purohit ◽  
A. N. Rajagopalan
Keyword(s):  
Input Image ◽  
Motion Blur ◽  
Dynamic Scene ◽  
Multi Scale ◽  
Prior Art ◽  
Motion Deblurring ◽  
Non Local ◽  
Model Size ◽  
Decoder Design ◽  
Spatially Varying

In this paper, we address the problem of dynamic scene deblurring in the presence of motion blur. Restoration of images affected by severe blur necessitates a network design with a large receptive field, which existing networks attempt to achieve through simple increment in the number of generic convolution layers, kernel-size, or the scales at which the image is processed. However, these techniques ignore the non-uniform nature of blur, and they come at the expense of an increase in model size and inference time. We present a new architecture composed of region adaptive dense deformable modules that implicitly discover the spatially varying shifts responsible for non-uniform blur in the input image and learn to modulate the filters. This capability is complemented by a self-attentive module which captures non-local spatial relationships among the intermediate features and enhances the spatially varying processing capability. We incorporate these modules into a densely connected encoder-decoder design which utilizes pre-trained Densenet filters to further improve the performance. Our network facilitates interpretable modeling of the spatially-varying deblurring process while dispensing with multi-scale processing and large filters entirely. Extensive comparisons with prior art on benchmark dynamic scene deblurring datasets clearly demonstrate the superiority of the proposed networks via significant improvements in accuracy and speed, enabling almost real-time deblurring.

scholarly journals Joint Motion Deblurring and Superresolution from Single Blurry Image

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Linyang He ◽  
Gang Li ◽  
Jinghong Liu
Keyword(s):  
Real Life ◽  
Kernel Estimation ◽  
Motion Blur ◽  
Joint Motion ◽  
Edge Preserving ◽  
Motion Deblurring ◽  
Blur Kernel Estimation ◽  
Blurred Image ◽  
Ill Posed ◽  
Sparse Kernel

Currently superresolution from a motion blurred image still remains a challenging task. The conventional approach, which preprocesses the blurry low resolution (LR) image with a deblurring algorithm and employs a superresolution algorithm, has the following limitation. The high frequency texture of the image is unavoidably lost in the deblurring process and this loss restricts the performance of the subsequent superresolution process. This paper presents a novel technique that performs motion deblurring and superresolution jointly from one single blurry image. The basic idea is to regularize the ill-posed reconstruction problem using an edge-preserving gradient prior and a sparse kernel prior. This method derives from an inverse problem approach under an efficient optimization scheme that alternates between blur kernel estimation and superresolving until convergence. Furthermore, this paper proposes a simple and efficient refinement formulation to remove artifacts and render better deblurred high resolution (HR) images. The improvements brought by the proposed combined framework are demonstrated by the processing results of both simulated and real-life images. Quantitative and qualitative results on challenging examples show that the proposed method outperforms the existing state-of-the-art methods and effectively eliminates motion blur and artifacts in the superresolved image.

Study on Motion Blur Image Restoration Algorithms

2013 ◽  
Vol 753-755 ◽  
pp. 2976-2979
Author(s):  
Yu Fan ◽  
Xue Feng Wu
Keyword(s):  
Mathematical Model ◽  
Image Restoration ◽  
Point Spread Function ◽  
Motion Blur ◽  
Inverse Filtering ◽  
Point Spread ◽  
Spread Function ◽  
Blurred Image ◽  
Restoration Algorithms ◽  
Blurred Images

The restore algorithm of the image blurred by motion is proposed, and a mathematical model based on motion blur system is eomtrueted£®The Point spread function of the motion blur is given£®According to the characteristics of blurred images£¬the parameters of point spread function are estimated ,and three methods are introduced for image restoration. The three methods are inverse filtering of image restoration,Lucy-Richardson image restoration and Wiener image restoration.The principles of the three image restoration methods are analyzed. The motion blurred image restoration experiment is made. The results show that the visibility of the image is improved ,and the image restoration is more stable.

scholarly journals Robust Visual Tracking Based on Fusional Multi-Correlation-Filters with a High-Confidence Judgement Mechanism

2020 ◽  
Vol 10 (6) ◽  
pp. 2151
Author(s):  
Wenbin Wang ◽  
Chao Liu ◽  
Bo Xu ◽  
Long Li ◽  
Wei Chen ◽  
...  
Keyword(s):  
Time Frame ◽  
Spatial Layout ◽  
Motion Blur ◽  
Frame Rate ◽  
Visual Object ◽  
Correlation Filters ◽  
High Confidence ◽  
Novel Approach ◽  
Highly Sensitive ◽  
Fast Motion

Visual object trackers based on correlation filters have recently demonstrated substantial robustness to challenging conditions with variations in illumination and motion blur. Nonetheless, the models depend strongly on the spatial layout and are highly sensitive to deformation, scale, and occlusion. As presented and discussed in this paper, the colour attributes are combined due to their complementary characteristics to handle variations in shape well. In addition, a novel approach for robust scale estimation is proposed for mitigatinge the problems caused by fast motion and scale variations. Moreover, feedback from high-confidence tracking results was also utilized to prevent model corruption. The evaluation results for our tracker demonstrate that it performed outstandingly in terms of both precision and accuracy with enhancements of approximately 25% and 49%, respectively, in authoritative benchmarks compared to those for other popular correlation- filter-based trackers. Finally, the proposed tracker has demonstrated strong robustness, which has enabled online object tracking under various scenarios at a real-time frame rate of approximately 65 frames per second (FPS).

Satellite image restoration in the context of a spatially varying point spread function

2010 ◽  
Vol 27 (6) ◽  
pp. 1473 ◽  
Author(s):  
Nasreddine Hajlaoui ◽  
Caroline Chaux ◽  
Guillaume Perrin ◽  
Frédéric Falzon ◽  
Amel Benazza-Benyahia
Keyword(s):  
Image Restoration ◽  
Point Spread Function ◽  
Satellite Image ◽  
Point Spread ◽  
Spread Function ◽  
Spatially Varying
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