An Efficient Blind Image Deblurring Using a Smoothing Function
This paper introduces an efficient deblurring image method based on a convolution-based and an iterative concept. Our method does not require specific conditions on images, so it can be widely applied for unspecific generic images. The kernel estimation is firstly performed and then will be used to...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2021/6684345 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832567892960346112 |
|---|---|
| author | Kittiya Khongkraphan Aniruth Phonon Sainuddeen Nuiphom |
| author_facet | Kittiya Khongkraphan Aniruth Phonon Sainuddeen Nuiphom |
| author_sort | Kittiya Khongkraphan |
| collection | DOAJ |
| description | This paper introduces an efficient deblurring image method based on a convolution-based and an iterative concept. Our method does not require specific conditions on images, so it can be widely applied for unspecific generic images. The kernel estimation is firstly performed and then will be used to estimate a latent image in each iteration. The final deblurred image is obtained from the convolution of the blurred image with the final estimated kernel. However, image deblurring is an ill-posed problem due to the nonuniqueness of solutions. Therefore, we propose a smoothing function, unlike previous approaches that applied piecewise functions on estimating a latent image. In our approach, we employ L2-regularization on intensity and gradient prior to converging to a solution of the deblurring problem. Moreover, our work is based on the quadratic splitting method. It guarantees that each subproblem has a closed-form solution. Various experiments on synthesized and real-world images confirm that our approach outperforms several existing methods, especially on the images corrupted by noises. Moreover, our method gives more reasonable and more natural deblurred images than those of other methods. |
| format | Article |
| id | doaj-art-b39dd27c74fc41ca96d7eec4229cadb3 |
| institution | Kabale University |
| issn | 1687-9732 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-b39dd27c74fc41ca96d7eec4229cadb32025-02-03T01:00:13ZengWileyApplied Computational Intelligence and Soft Computing1687-97322021-01-01202110.1155/2021/66843456684345An Efficient Blind Image Deblurring Using a Smoothing FunctionKittiya Khongkraphan0Aniruth Phonon1Sainuddeen Nuiphom2Faculty of Science and TechnologyFaculty of Science and TechnologyFaculty of Science and TechnologyThis paper introduces an efficient deblurring image method based on a convolution-based and an iterative concept. Our method does not require specific conditions on images, so it can be widely applied for unspecific generic images. The kernel estimation is firstly performed and then will be used to estimate a latent image in each iteration. The final deblurred image is obtained from the convolution of the blurred image with the final estimated kernel. However, image deblurring is an ill-posed problem due to the nonuniqueness of solutions. Therefore, we propose a smoothing function, unlike previous approaches that applied piecewise functions on estimating a latent image. In our approach, we employ L2-regularization on intensity and gradient prior to converging to a solution of the deblurring problem. Moreover, our work is based on the quadratic splitting method. It guarantees that each subproblem has a closed-form solution. Various experiments on synthesized and real-world images confirm that our approach outperforms several existing methods, especially on the images corrupted by noises. Moreover, our method gives more reasonable and more natural deblurred images than those of other methods.http://dx.doi.org/10.1155/2021/6684345 |
| spellingShingle | Kittiya Khongkraphan Aniruth Phonon Sainuddeen Nuiphom An Efficient Blind Image Deblurring Using a Smoothing Function Applied Computational Intelligence and Soft Computing |
| title | An Efficient Blind Image Deblurring Using a Smoothing Function |
| title_full | An Efficient Blind Image Deblurring Using a Smoothing Function |
| title_fullStr | An Efficient Blind Image Deblurring Using a Smoothing Function |
| title_full_unstemmed | An Efficient Blind Image Deblurring Using a Smoothing Function |
| title_short | An Efficient Blind Image Deblurring Using a Smoothing Function |
| title_sort | efficient blind image deblurring using a smoothing function |
| url | http://dx.doi.org/10.1155/2021/6684345 |
| work_keys_str_mv | AT kittiyakhongkraphan anefficientblindimagedeblurringusingasmoothingfunction AT aniruthphonon anefficientblindimagedeblurringusingasmoothingfunction AT sainuddeennuiphom anefficientblindimagedeblurringusingasmoothingfunction AT kittiyakhongkraphan efficientblindimagedeblurringusingasmoothingfunction AT aniruthphonon efficientblindimagedeblurringusingasmoothingfunction AT sainuddeennuiphom efficientblindimagedeblurringusingasmoothingfunction |