Restoration of Motion-Blurred Image Based on Border Deformation Detection: A Traffic Sign Restoration Model

Zeng, Yiliang; Lan, Jinhui; Wang, Qi; Gao, Jing

doi:10.1371/journal.pone.0120885

Cited by 14 publications

(6 citation statements)

References 24 publications

(26 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The generation of CT images can be modeled as an image degradation system. The motion-degraded image is formed by a convolution between the ideal image and the point spread function (PSF) [23].…”

Section: Image Degradation Modelmentioning

confidence: 99%

A Rigid Motion Artifact Reduction Method for CT Based on Blind Deconvolution

Zhang

2019

Algorithms

View full text Add to dashboard Cite

In computed tomography (CT), artifacts due to patient rigid motion often significantly degrade image quality. This paper suggests a method based on iterative blind deconvolution to eliminate motion artifacts. The proposed method alternately reconstructs the image and reduces motion artifacts in an iterative scheme until the difference measure between two successive iterations is smaller than a threshold. In this iterative process, Richardson–Lucy (RL) deconvolution with spatially adaptive total variation (SATV) regularization is inserted into the iterative process of the ordered subsets expectation maximization (OSEM) reconstruction algorithm. The proposed method is evaluated on a numerical phantom, a head phantom, and patient scan. The reconstructed images indicate that the proposed method can reduce motion artifacts and provide high-quality images. Quantitative evaluations also show the proposed method yielded an appreciable improvement on all metrics, reducing root-mean-square error (RMSE) by about 30% and increasing Pearson correlation coefficient (CC) and mean structural similarity (MSSIM) by about 15% and 20%, respectively, compared to the RL-OSEM method. Furthermore, the proposed method only needs measured raw data and no additional measurements are needed. Compared with the previous work, it can be applied to any scanning mode and can realize six degrees of freedom motion artifact reduction, so the artifact reduction effect is better in clinical experiments.

show abstract

Section: Image Degradation Modelmentioning

confidence: 99%

A Rigid Motion Artifact Reduction Method for CT Based on Blind Deconvolution

Zhang

2019

Algorithms

View full text Add to dashboard Cite

show abstract

“…In recent years, several studies have proposed the intelligent traffic sign classification systems to classify ideogram-based traffic signs in real-time [16,[26][27][28][29][30]. Ohgushi et al [27] developed a traffic sign classification system that utilized color information and Bags of Features (BoF) using SVM classifier to classify traffic signs.…”

Section: Related Workmentioning

confidence: 99%

Recognition of Traffic Sign Based on Bag-of-Words and Artificial Neural Network

Islam¹,

Raj

Mujtaba³

2017

Symmetry

View full text Add to dashboard Cite

Abstract:The traffic sign recognition system is a support system that can be useful to give notification and warning to drivers. It may be effective for traffic conditions on the current road traffic system. A robust artificial intelligence based traffic sign recognition system can support the driver and significantly reduce driving risk and injury. It performs by recognizing and interpreting various traffic sign using vision-based information. This study aims to recognize the wellmaintained, un-maintained, standard, and non-standard traffic signs using the Bag-of-Words and the Artificial Neural Network techniques. This research work employs a Bag-of-Words model on the Speeded Up Robust Features descriptors of the road traffic signs. A robust classifier Artificial Neural Network has been employed to recognize the traffic sign in its respective class. The proposed system has been trained and tested to determine the suitable neural network architecture. The experimental results showed high accuracy of classification of traffic signs including complex background images. The proposed traffic sign detection and recognition system obtained 99.00% classification accuracy with a 1.00% false positive rate. For real-time implementation and deployment, this marginal false positive rate may increase reliability and stability of the proposed system.

show abstract

“…As one of the main causes of image degradation, it seriously affects the performances of computer vision system in various fields. So efficient motion deblurring technology is conducive to improving the reliability of related applications, such as aerospace, medical imaging, traffic monitoring, public safety, military search, satellite and space image [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Improved estimation of motion blur parameters for restoration from a single image

et al. 2020

View full text Add to dashboard Cite

This paper presents an improved method to estimate the blur parameters of motion deblurring algorithm for single image restoration based on the point spread function (PSF) in frequency spectrum. We then introduce a modification to the Radon transform in the blur angle estimation scheme with our proposed difference value vs angle curve. Subsequently, the auto-correlation matrix is employed to estimate the blur angle by measuring the distance between the conjugated-correlated troughs. Finally, we evaluate the accuracy, robustness and time efficiency of our proposed method with the existing algorithms on the public benchmarks and the natural real motion blurred images. The experimental results demonstrate that the proposed PSF estimation scheme not only could obtain a higher accuracy for the blur angle and blur length, but also demonstrate stronger robustness and higher time efficiency under different circumstances.

show abstract

Restoration of Motion-Blurred Image Based on Border Deformation Detection: A Traffic Sign Restoration Model

Cited by 14 publications

References 24 publications

A Rigid Motion Artifact Reduction Method for CT Based on Blind Deconvolution

A Rigid Motion Artifact Reduction Method for CT Based on Blind Deconvolution

Recognition of Traffic Sign Based on Bag-of-Words and Artificial Neural Network

Improved estimation of motion blur parameters for restoration from a single image

Contact Info

Product

Resources

About