Blind Text Image Deblurring Algorithm Based on Multi-Scale Fusion and Sparse Priors
The goal of blind text image deblurring is to obtain a clean text image from the given blurry text image without knowing the blur kernel. Sparsity-based methods have been shown their effectiveness in various blind text image deblurring models. However, the blur kernel estimation methods based on spa...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10044695/ |
| _version_ | 1797900252056911872 |
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| author | Zhe Li Ming Yang Libo Cheng Xiaoning Jia |
| author_facet | Zhe Li Ming Yang Libo Cheng Xiaoning Jia |
| author_sort | Zhe Li |
| collection | DOAJ |
| description | The goal of blind text image deblurring is to obtain a clean text image from the given blurry text image without knowing the blur kernel. Sparsity-based methods have been shown their effectiveness in various blind text image deblurring models. However, the blur kernel estimation methods based on sparse priors lack of the consideration for the brightness information about the blur kernel, which will affect the restoration effect of the blur kernel. Besides, previous methods seldom apply sparse priors to both spatial domain and transform domain information. We propose a novel blind text image deblurring model based on multi-scale fusion and sparse priors. Besides the sparse gradient prior on the latent clean text image, we add the sparse prior on the high-frequency wavelet coefficients of the latent text image, which will better constrain the solution space and obtain good clean images. The semi-quadratic splitting method is used to alternately optimize the blur kernel and the latent clean image. Meanwhile, we consider the influence of the brightness feature of the restored blur kernel. By multi-scale fusion technique on the basis of Laplacian weight and saliency weight, we fuse the computed blur kernels in three channels to improve the quality of blur kernel. The experimental results show that our algorithm has good results in the restoration of blur kernels and text images. |
| first_indexed | 2024-04-10T08:43:05Z |
| format | Article |
| id | doaj.art-dee62f38143c422fb859663ab9b86c7a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T08:43:05Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-dee62f38143c422fb859663ab9b86c7a2023-02-23T00:01:08ZengIEEEIEEE Access2169-35362023-01-0111160421605510.1109/ACCESS.2023.324515010044695Blind Text Image Deblurring Algorithm Based on Multi-Scale Fusion and Sparse PriorsZhe Li0https://orcid.org/0000-0001-5424-2695Ming Yang1https://orcid.org/0000-0003-3578-6800Libo Cheng2https://orcid.org/0000-0003-1452-6508Xiaoning Jia3https://orcid.org/0000-0002-0859-6561School of Mathematics and Statistics, Changchun University of Science and Technology, Changchun, ChinaSchool of Mathematics and Statistics, Changchun University of Science and Technology, Changchun, ChinaSchool of Mathematics and Statistics, Changchun University of Science and Technology, Changchun, ChinaSchool of Mathematics and Statistics, Changchun University of Science and Technology, Changchun, ChinaThe goal of blind text image deblurring is to obtain a clean text image from the given blurry text image without knowing the blur kernel. Sparsity-based methods have been shown their effectiveness in various blind text image deblurring models. However, the blur kernel estimation methods based on sparse priors lack of the consideration for the brightness information about the blur kernel, which will affect the restoration effect of the blur kernel. Besides, previous methods seldom apply sparse priors to both spatial domain and transform domain information. We propose a novel blind text image deblurring model based on multi-scale fusion and sparse priors. Besides the sparse gradient prior on the latent clean text image, we add the sparse prior on the high-frequency wavelet coefficients of the latent text image, which will better constrain the solution space and obtain good clean images. The semi-quadratic splitting method is used to alternately optimize the blur kernel and the latent clean image. Meanwhile, we consider the influence of the brightness feature of the restored blur kernel. By multi-scale fusion technique on the basis of Laplacian weight and saliency weight, we fuse the computed blur kernels in three channels to improve the quality of blur kernel. The experimental results show that our algorithm has good results in the restoration of blur kernels and text images.https://ieeexplore.ieee.org/document/10044695/Blind text image deblurringsparse priorsmulti-scale fusionwavelet transform |
| spellingShingle | Zhe Li Ming Yang Libo Cheng Xiaoning Jia Blind Text Image Deblurring Algorithm Based on Multi-Scale Fusion and Sparse Priors IEEE Access Blind text image deblurring sparse priors multi-scale fusion wavelet transform |
| title | Blind Text Image Deblurring Algorithm Based on Multi-Scale Fusion and Sparse Priors |
| title_full | Blind Text Image Deblurring Algorithm Based on Multi-Scale Fusion and Sparse Priors |
| title_fullStr | Blind Text Image Deblurring Algorithm Based on Multi-Scale Fusion and Sparse Priors |
| title_full_unstemmed | Blind Text Image Deblurring Algorithm Based on Multi-Scale Fusion and Sparse Priors |
| title_short | Blind Text Image Deblurring Algorithm Based on Multi-Scale Fusion and Sparse Priors |
| title_sort | blind text image deblurring algorithm based on multi scale fusion and sparse priors |
| topic | Blind text image deblurring sparse priors multi-scale fusion wavelet transform |
| url | https://ieeexplore.ieee.org/document/10044695/ |
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