Image Deblurring for Navigation Systems of Vision Impaired People Using Sensor Fusion Data
Image deblurring is a key component in vision based indoor/outdoor navigation systems; as blurring is one of the main causes of poor image quality. When images with poor quality are used for analysis, navigation errors are likely to be generated. For navigation systems, camera movement mainly causes...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE
2014
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/42321 |
| _version_ | 1848756387646013440 |
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| author | Rajakaruna, Rajakuruna Rathnayake, Rathnayake Chan, Kit Yan Murray, Iain |
| author2 | IEEE |
| author_facet | IEEE Rajakaruna, Rajakuruna Rathnayake, Rathnayake Chan, Kit Yan Murray, Iain |
| author_sort | Rajakaruna, Rajakuruna |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Image deblurring is a key component in vision based indoor/outdoor navigation systems; as blurring is one of the main causes of poor image quality. When images with poor quality are used for analysis, navigation errors are likely to be generated. For navigation systems, camera movement mainly causes blurring, as the camera is continuously moving by the body movement. This paper proposes a deblurring methodology that takes advantage of the fact that most smartphones are equipped with 3-axis accelerometers and gyroscopes. It uses data of the accelerometer and gyroscope to derive a motion vector calculated from the motion of the smartphone during the image-capturing period. A heuristic method, namely particle swarm optimization, is developed to determine the optimal motion vector, in order to deblur the captured image by reversing the effect of motion. Experimental results indicated that deblurring can be successfully performed using the optimal motion vector and that the deblurred images can be used as a readily approach to object and path identification in vision based navigation systems, especially for blind and vision impaired indoor/outdoor navigation. Also, the performance of proposed method is compared with the commonly used deblurring methods. Better results in term of image quality can be achieved. This experiment aims to identify issues in image quality including low light conditions, low quality images due to movement of the capture device and static and moving obstacles in front of the user in both indoor and outdoor environments. From this information, image-processing techniques to will be identified to assist in object and path edge detection necessary to create a guidance system for those with low vision. |
| first_indexed | 2025-11-14T09:11:24Z |
| format | Conference Paper |
| id | curtin-20.500.11937-42321 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:11:24Z |
| publishDate | 2014 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-423212017-09-13T14:20:14Z Image Deblurring for Navigation Systems of Vision Impaired People Using Sensor Fusion Data Rajakaruna, Rajakuruna Rathnayake, Rathnayake Chan, Kit Yan Murray, Iain IEEE particle swarm optimization vision impaired navigation inertial sensors image deblurring Image deblurring is a key component in vision based indoor/outdoor navigation systems; as blurring is one of the main causes of poor image quality. When images with poor quality are used for analysis, navigation errors are likely to be generated. For navigation systems, camera movement mainly causes blurring, as the camera is continuously moving by the body movement. This paper proposes a deblurring methodology that takes advantage of the fact that most smartphones are equipped with 3-axis accelerometers and gyroscopes. It uses data of the accelerometer and gyroscope to derive a motion vector calculated from the motion of the smartphone during the image-capturing period. A heuristic method, namely particle swarm optimization, is developed to determine the optimal motion vector, in order to deblur the captured image by reversing the effect of motion. Experimental results indicated that deblurring can be successfully performed using the optimal motion vector and that the deblurred images can be used as a readily approach to object and path identification in vision based navigation systems, especially for blind and vision impaired indoor/outdoor navigation. Also, the performance of proposed method is compared with the commonly used deblurring methods. Better results in term of image quality can be achieved. This experiment aims to identify issues in image quality including low light conditions, low quality images due to movement of the capture device and static and moving obstacles in front of the user in both indoor and outdoor environments. From this information, image-processing techniques to will be identified to assist in object and path edge detection necessary to create a guidance system for those with low vision. 2014 Conference Paper http://hdl.handle.net/20.500.11937/42321 10.1109/ISSNIP.2014.6827599 IEEE fulltext |
| spellingShingle | particle swarm optimization vision impaired navigation inertial sensors image deblurring Rajakaruna, Rajakuruna Rathnayake, Rathnayake Chan, Kit Yan Murray, Iain Image Deblurring for Navigation Systems of Vision Impaired People Using Sensor Fusion Data |
| title | Image Deblurring for Navigation Systems of Vision Impaired People Using Sensor Fusion Data |
| title_full | Image Deblurring for Navigation Systems of Vision Impaired People Using Sensor Fusion Data |
| title_fullStr | Image Deblurring for Navigation Systems of Vision Impaired People Using Sensor Fusion Data |
| title_full_unstemmed | Image Deblurring for Navigation Systems of Vision Impaired People Using Sensor Fusion Data |
| title_short | Image Deblurring for Navigation Systems of Vision Impaired People Using Sensor Fusion Data |
| title_sort | image deblurring for navigation systems of vision impaired people using sensor fusion data |
| topic | particle swarm optimization vision impaired navigation inertial sensors image deblurring |
| url | http://hdl.handle.net/20.500.11937/42321 |