Image Deblurring using a Hybrid Optimization Algorithm
In many applications, such as way finding and navigation, the quality of image sequences are generally poor, as motion blur caused from body movement degrades image quality. It is difficult to remove the blurs without prior information about the camera motion. In this paper, we utilize inertial sens...
| Main Authors: | , , , |
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| Format: | Conference Paper |
| Published: |
IEEE
2014
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/42098 |
| _version_ | 1848756326682853376 |
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| author | Chan, Kit Yan Rajakaruna, Rajakuruna Rathnayake, Rathnayake Murray, Iain |
| author2 | IEEE |
| author_facet | IEEE Chan, Kit Yan Rajakaruna, Rajakuruna Rathnayake, Rathnayake Murray, Iain |
| author_sort | Chan, Kit Yan |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In many applications, such as way finding and navigation, the quality of image sequences are generally poor, as motion blur caused from body movement degrades image quality. It is difficult to remove the blurs without prior information about the camera motion. In this paper, we utilize inertial sensors, including accelerometers and gyroscopes, installed in smartphones, in order to determine geometric data of camera motion during exposure. Based on the geometric data, we derive a blurring function namely point spread function (PSF) which deblur the captured image by reversing motion effect. However, determination of the optimal PSF with respect to the image quality is multioptimum, as deblurred images are not linearly correlated to image intelligibility. Therefore, this paper proposes a hybrid optimization method, which is, incorporated the mechanisms of particle swarm optimization (PSO) and gradient search method, in order to optimize PSF parameters. It aims to incorporate the advantages of the two methods, where the PSO is effective in localizing the global region and the gradient search method is effective in converging local optimum. Experimental results indicated that deblurring can be successfully performed using the optimal PSF. Also, the performance of proposed method is compared with the commonly used deblurring methods. Better results in term of image quality can be achieved. The resulting deblurring methodology is an important component. It will be used to improve deblurred images to perform edge detection, in order to detect paths, stairs ways, movable and immovable objects for vision-impaired people. |
| first_indexed | 2025-11-14T09:10:25Z |
| format | Conference Paper |
| id | curtin-20.500.11937-42098 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:10:25Z |
| publishDate | 2014 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-420982017-09-13T14:22:51Z Image Deblurring using a Hybrid Optimization Algorithm Chan, Kit Yan Rajakaruna, Rajakuruna Rathnayake, Rathnayake Murray, Iain IEEE particle swarm optimization inertial sensors vision impaired navigation image deblurring hybrid optimization method In many applications, such as way finding and navigation, the quality of image sequences are generally poor, as motion blur caused from body movement degrades image quality. It is difficult to remove the blurs without prior information about the camera motion. In this paper, we utilize inertial sensors, including accelerometers and gyroscopes, installed in smartphones, in order to determine geometric data of camera motion during exposure. Based on the geometric data, we derive a blurring function namely point spread function (PSF) which deblur the captured image by reversing motion effect. However, determination of the optimal PSF with respect to the image quality is multioptimum, as deblurred images are not linearly correlated to image intelligibility. Therefore, this paper proposes a hybrid optimization method, which is, incorporated the mechanisms of particle swarm optimization (PSO) and gradient search method, in order to optimize PSF parameters. It aims to incorporate the advantages of the two methods, where the PSO is effective in localizing the global region and the gradient search method is effective in converging local optimum. Experimental results indicated that deblurring can be successfully performed using the optimal PSF. Also, the performance of proposed method is compared with the commonly used deblurring methods. Better results in term of image quality can be achieved. The resulting deblurring methodology is an important component. It will be used to improve deblurred images to perform edge detection, in order to detect paths, stairs ways, movable and immovable objects for vision-impaired people. 2014 Conference Paper http://hdl.handle.net/20.500.11937/42098 10.1109/CEC.2014.6900266 IEEE restricted |
| spellingShingle | particle swarm optimization inertial sensors vision impaired navigation image deblurring hybrid optimization method Chan, Kit Yan Rajakaruna, Rajakuruna Rathnayake, Rathnayake Murray, Iain Image Deblurring using a Hybrid Optimization Algorithm |
| title | Image Deblurring using a Hybrid Optimization Algorithm |
| title_full | Image Deblurring using a Hybrid Optimization Algorithm |
| title_fullStr | Image Deblurring using a Hybrid Optimization Algorithm |
| title_full_unstemmed | Image Deblurring using a Hybrid Optimization Algorithm |
| title_short | Image Deblurring using a Hybrid Optimization Algorithm |
| title_sort | image deblurring using a hybrid optimization algorithm |
| topic | particle swarm optimization inertial sensors vision impaired navigation image deblurring hybrid optimization method |
| url | http://hdl.handle.net/20.500.11937/42098 |