Blind Deblurring and Denoising of Images Corrupted by Unidirectional Object Motion Blur and Sensor Noise

2016 ◽  
Vol 25 (9) ◽  
pp. 4129-4144 ◽  
Author(s):  
Yi Zhang ◽  
Keigo Hirakawa
Keyword(s):  
Motion Blur ◽  
Object Motion ◽  
Sensor Noise ◽  
Blind Deblurring ◽  
Deblurring And Denoising

scholarly journals Tracking by Deblatting

2021 ◽  
Author(s):  
Denys Rozumnyi ◽  
Jan Kotera ◽  
Filip Šroubek ◽  
Jiří Matas
Keyword(s):  
High Speed ◽  
High Performance ◽  
Motion Blur ◽  
Velocity Estimation ◽  
High Speed Camera ◽  
Trajectory Function ◽  
Novel Approach ◽  
Blind Deblurring ◽  
Object Trajectories ◽  
Complex Trajectories

AbstractObjects moving at high speed along complex trajectories often appear in videos, especially videos of sports. Such objects travel a considerable distance during exposure time of a single frame, and therefore, their position in the frame is not well defined. They appear as semi-transparent streaks due to the motion blur and cannot be reliably tracked by general trackers. We propose a novel approach called Tracking by Deblatting based on the observation that motion blur is directly related to the intra-frame trajectory of an object. Blur is estimated by solving two intertwined inverse problems, blind deblurring and image matting, which we call deblatting. By postprocessing, non-causal Tracking by Deblatting estimates continuous, complete, and accurate object trajectories for the whole sequence. Tracked objects are precisely localized with higher temporal resolution than by conventional trackers. Energy minimization by dynamic programming is used to detect abrupt changes of motion, called bounces. High-order polynomials are then fitted to smooth trajectory segments between bounces. The output is a continuous trajectory function that assigns location for every real-valued time stamp from zero to the number of frames. The proposed algorithm was evaluated on a newly created dataset of videos from a high-speed camera using a novel Trajectory-IoU metric that generalizes the traditional Intersection over Union and measures the accuracy of the intra-frame trajectory. The proposed method outperforms the baselines both in recall and trajectory accuracy. Additionally, we show that from the trajectory function precise physical calculations are possible, such as radius, gravity, and sub-frame object velocity. Velocity estimation is compared to the high-speed camera measurements and radars. Results show high performance of the proposed method in terms of Trajectory-IoU, recall, and velocity estimation.

Video-based non-uniform object motion blur estimation and deblurring

2012 ◽  
Vol 86 ◽  
pp. 170-178 ◽  
Author(s):  
Xiaoyu Deng ◽  
Yan Shen ◽  
Mingli Song ◽  
Dacheng Tao ◽  
Jiajun Bu ◽  
...  
Keyword(s):  
Motion Blur ◽  
Object Motion ◽  
Blur Estimation

scholarly journals Restoration of Noisy Blurred Images Using MFPIA and Discrete Wavelet Transform

2013 ◽  
Vol 9 (1) ◽  
pp. 1-15
Author(s):  
Dunia Tahir
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Gaussian Noise ◽  
Motion Blur ◽  
Discrete Wavelet ◽  
Wavelet Thresholding ◽  
Fixed Phase ◽  
Approximation Coefficients ◽  
Blurred Images ◽  
Deblurring And Denoising

In this paper, image deblurring and denoising are presented. The used images were blurred either with Gaussian or motion blur and corrupted either by Gaussian noise or by salt & pepper noise. In our algorithm, the modified fixed-phase iterative algorithm (MFPIA) is used to reduce the blur. Then a discrete wavelet transform is used to divide the image into two parts. The first part represents the approximation coefficients. While the second part represents the detail coefficients, that a noise is removed by using the BayesShrink wavelet thresholding method.

scholarly journals A Dynamic Data Driven and Data Segregation Approach Image Restoration Using Neural Networks

2021 ◽  
Vol 11 (2) ◽  
pp. 843-849
Author(s):  
A. Gnanasekar ◽  
S. Selvi ◽  
A.S.U. Soundharyaa ◽  
A. Malini ◽  
K.R. Ramya
Keyword(s):  
Image Restoration ◽  
Low Frequency ◽  
Frequency Component ◽  
Motion Blur ◽  
Object Motion ◽  
High Frequency Component ◽  
Camera Shake ◽  
Pattern Layer ◽  
Energy Signal ◽  
The Impact

Image restoration is the method of restoring an image to its original state by removing noise and blur. Image disclarity is crucial to maintain in a variety of cases, including photography, where motion blur is caused by camera shake when taking images, radar imaging, where the impact of image system reaction is removed, and so on. Image noise is an unwanted signal that appears in an image from a sensor, such as a power / energy signal, or from the atmosphere, such as rain or snow. Coding artefacts, resolution limitations, transmission noise, object motion, camera shake, or a confluence of events could cause image degradation. With the intention of separating HF and LF objects, image decomposition is used to decompose the distorted image into a pattern layer (High Frequency Component) and a framework layer (Low Frequency Component).

scholarly journals Blind Deblurring of Saturated Images Based on Optimization and Deep Learning for Dynamic Visual Inspection on the Assembly Line

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 678 ◽  
Author(s):  
Bodi Wang ◽  
Guixiong Liu ◽  
Junfang Wu
Keyword(s):  
Deep Learning ◽  
Visual Inspection ◽  
Kernel Estimation ◽  
Visual Quality ◽  
Motion Blur ◽  
Blind Deblurring ◽  
Blur Kernel Estimation ◽  
Wide Range ◽  
Good Score ◽  
Blur Kernel

Image deblurring can improve visual quality and mitigates motion blur for dynamic visual inspection. We propose a method to deblur saturated images for dynamic visual inspection by applying blur kernel estimation and deconvolution modeling. The blur kernel is estimated in a transform domain, whereas the deconvolution model is decoupled into deblurring and denoising stages via variable splitting. Deblurring predicts the mask specifying saturated pixels, which are then discarded, and denoising is learned via the fast and flexible denoising network (FFDNet) convolutional neural network (CNN) at a wide range of noise levels. Hence, the proposed deconvolution model provides the benefits of both model optimization and deep learning. Experiments demonstrate that the proposed method suitably restores visual quality and outperforms existing approaches with good score improvements.

Quasi-Robust Blind Deblurring with Multiple Color Images

2011 ◽  
Vol 121-126 ◽  
pp. 1605-1609 ◽  
Author(s):  
Rui Hua Liu ◽  
Fang Li ◽  
Li Yun Su
Keyword(s):  
Color Images ◽  
L1 Norm ◽  
New Model ◽  
Blind Deblurring ◽  
L2 Norm ◽  
Degraded Images ◽  
Deblurring And Denoising

In this paper, we firstly introduce the limitation and deficiency of L1-norm and L2-norm in deblurring and denoising and the merit of quasi-robust function. Then, we propose a new blind deblurring model using multiple color images. Finally, we give some simulations and the results show the effect of our new model for color degraded images. To see how well our algorithm compared against the non-blind deblurring model.

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