Multi-frame Image Super-resolution Reconstruction Using Multi-grained Cascade Forest
Super-resolution image reconstruction utilizes two algorithms, where one is for single-frame image reconstruction, and the other is for multi-frame image reconstruction. Singleframe image reconstruction generally takes the first degradation and is followed by reconstruction, which essentially create...
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| Format: | Article |
| Language: | English |
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Polish Academy of Sciences
2019-11-01
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| Series: | International Journal of Electronics and Telecommunications |
| Subjects: | |
| Online Access: | https://journals.pan.pl/Content/113729/PDF/91.pdf |
| _version_ | 1798040711357005824 |
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| author | Yaming Wang Zhikang Luo Wenqing Huang |
| author_facet | Yaming Wang Zhikang Luo Wenqing Huang |
| author_sort | Yaming Wang |
| collection | DOAJ |
| description | Super-resolution image reconstruction utilizes two algorithms, where one is for single-frame image reconstruction, and the other is for multi-frame image reconstruction. Singleframe image reconstruction generally takes the first degradation and is followed by reconstruction, which essentially creates a problem of insufficient characterization. Multi-frame images provide additional information for image reconstruction relative to single frame images due to the slight differences between sequential frames. However, the existing super-resolution algorithm for multi-frame images do not take advantage of this key factor, either because of loose structure and complexity, or because the individual frames are restored poorly. This paper proposes a new SR reconstruction algorithm for images using Multi-grained Cascade Forest. Multi-frame image reconstruction is processed sequentially. Firstly, the image registration algorithm uses a convolutional neural network to register low-resolution image sequences, and then the images are reconstructed after registration by the Multi-grained Cascade Forest reconstruction algorithm. Finally, the reconstructed images are fused. The optimal algorithm is selected for each step to get the most out of the details and tightly connect the internal logic of each sequential step. This novel approach proposed in this paper, in which the depth of the cascade forest is procedurally generated for recovered images, rather than being a constant. After training each layer, the recovered image is automatically evaluated, and new layers are constructed for training until an optimal restored image is obtained. Experiments show that this method improves the quality of image reconstruction while preserving the details of the image. |
| first_indexed | 2024-04-11T22:11:21Z |
| format | Article |
| id | doaj.art-06db4e6b0c524c90bb01ae629bb1db56 |
| institution | Directory Open Access Journal |
| issn | 2081-8491 2300-1933 |
| language | English |
| last_indexed | 2024-04-11T22:11:21Z |
| publishDate | 2019-11-01 |
| publisher | Polish Academy of Sciences |
| record_format | Article |
| series | International Journal of Electronics and Telecommunications |
| spelling | doaj.art-06db4e6b0c524c90bb01ae629bb1db562022-12-22T04:00:32ZengPolish Academy of SciencesInternational Journal of Electronics and Telecommunications2081-84912300-19332019-11-01vol. 65No 4687692https://doi.org/10.24425/ijet.2019.130250Multi-frame Image Super-resolution Reconstruction Using Multi-grained Cascade ForestYaming WangZhikang LuoWenqing HuangSuper-resolution image reconstruction utilizes two algorithms, where one is for single-frame image reconstruction, and the other is for multi-frame image reconstruction. Singleframe image reconstruction generally takes the first degradation and is followed by reconstruction, which essentially creates a problem of insufficient characterization. Multi-frame images provide additional information for image reconstruction relative to single frame images due to the slight differences between sequential frames. However, the existing super-resolution algorithm for multi-frame images do not take advantage of this key factor, either because of loose structure and complexity, or because the individual frames are restored poorly. This paper proposes a new SR reconstruction algorithm for images using Multi-grained Cascade Forest. Multi-frame image reconstruction is processed sequentially. Firstly, the image registration algorithm uses a convolutional neural network to register low-resolution image sequences, and then the images are reconstructed after registration by the Multi-grained Cascade Forest reconstruction algorithm. Finally, the reconstructed images are fused. The optimal algorithm is selected for each step to get the most out of the details and tightly connect the internal logic of each sequential step. This novel approach proposed in this paper, in which the depth of the cascade forest is procedurally generated for recovered images, rather than being a constant. After training each layer, the recovered image is automatically evaluated, and new layers are constructed for training until an optimal restored image is obtained. Experiments show that this method improves the quality of image reconstruction while preserving the details of the image.https://journals.pan.pl/Content/113729/PDF/91.pdfmulti-frame image srimage registrationsrmcfimage fusion |
| spellingShingle | Yaming Wang Zhikang Luo Wenqing Huang Multi-frame Image Super-resolution Reconstruction Using Multi-grained Cascade Forest International Journal of Electronics and Telecommunications multi-frame image sr image registration srmcf image fusion |
| title | Multi-frame Image Super-resolution Reconstruction Using Multi-grained Cascade Forest |
| title_full | Multi-frame Image Super-resolution Reconstruction Using Multi-grained Cascade Forest |
| title_fullStr | Multi-frame Image Super-resolution Reconstruction Using Multi-grained Cascade Forest |
| title_full_unstemmed | Multi-frame Image Super-resolution Reconstruction Using Multi-grained Cascade Forest |
| title_short | Multi-frame Image Super-resolution Reconstruction Using Multi-grained Cascade Forest |
| title_sort | multi frame image super resolution reconstruction using multi grained cascade forest |
| topic | multi-frame image sr image registration srmcf image fusion |
| url | https://journals.pan.pl/Content/113729/PDF/91.pdf |
| work_keys_str_mv | AT yamingwang multiframeimagesuperresolutionreconstructionusingmultigrainedcascadeforest AT zhikangluo multiframeimagesuperresolutionreconstructionusingmultigrainedcascadeforest AT wenqinghuang multiframeimagesuperresolutionreconstructionusingmultigrainedcascadeforest |