Development of High Performance Quantum Image Algorithm on Constrained Least Squares Filtering Computation
Recent development of computer technology may lead to the quantum image algorithms becoming a hotspot. Quantum information and computation give some advantages to our quantum image algorithms, which deal with the limited problems that cannot be solved by the original classical image algorithm. Image...
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MDPI AG
2020-10-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/22/11/1207 |
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| author | Shumei Wang Pengao Xu Ruicheng Song Peiyao Li Hongyang Ma |
| author_facet | Shumei Wang Pengao Xu Ruicheng Song Peiyao Li Hongyang Ma |
| author_sort | Shumei Wang |
| collection | DOAJ |
| description | Recent development of computer technology may lead to the quantum image algorithms becoming a hotspot. Quantum information and computation give some advantages to our quantum image algorithms, which deal with the limited problems that cannot be solved by the original classical image algorithm. Image processing cry out for applications of quantum image. Most works on quantum images are theoretical or sometimes even unpolished, although real-world experiments in quantum computer have begun and are multiplying. However, just as the development of computer technology helped to drive the Technology Revolution, a new quantum image algorithm on constrained least squares filtering computation was proposed from quantum mechanics, quantum information, and extremely powerful computer. A quantum image representation model is introduced to construct an image model, which is then used for image processing. Prior knowledge is employed in order to reconstruct or estimate the point spread function, and a non-degenerate estimate is obtained based on the opposite processing. The fuzzy function against noises is solved using the optimal measure of smoothness. On the constraint condition, determine the minimum criterion function and estimate the original image function. For some motion blurs and some kinds of noise pollutions, such as Gaussian noises, the proposed algorithm is able to yield better recovery results. Additionally, it should be noted that, when there is a noise attack with very low noise intensity, the model based on the constrained least squares filtering can still deliver good recovery results, with strong robustness. Subsequently, discuss the simulation analysis of the complexity of implementing quantum circuits and image filtering, and demonstrate that the algorithm has a good effect on fuzzy recovery, when the noise density is small. |
| first_indexed | 2024-03-10T15:20:38Z |
| format | Article |
| id | doaj.art-4952a7312e8c4bf49f03217345a06e65 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T15:20:38Z |
| publishDate | 2020-10-01 |
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| spelling | doaj.art-4952a7312e8c4bf49f03217345a06e652023-11-20T18:29:17ZengMDPI AGEntropy1099-43002020-10-012211120710.3390/e22111207Development of High Performance Quantum Image Algorithm on Constrained Least Squares Filtering ComputationShumei Wang0Pengao Xu1Ruicheng Song2Peiyao Li3Hongyang Ma4Quantum Physics Laboratory, School of Sciences, Qingdao University of Technology, Qingdao 266520, ChinaQuantum Physics Laboratory, School of Sciences, Qingdao University of Technology, Qingdao 266520, ChinaQuantum Physics Laboratory, School of Sciences, Qingdao University of Technology, Qingdao 266520, ChinaQuantum Physics Laboratory, School of Sciences, Qingdao University of Technology, Qingdao 266520, ChinaQuantum Physics Laboratory, School of Sciences, Qingdao University of Technology, Qingdao 266520, ChinaRecent development of computer technology may lead to the quantum image algorithms becoming a hotspot. Quantum information and computation give some advantages to our quantum image algorithms, which deal with the limited problems that cannot be solved by the original classical image algorithm. Image processing cry out for applications of quantum image. Most works on quantum images are theoretical or sometimes even unpolished, although real-world experiments in quantum computer have begun and are multiplying. However, just as the development of computer technology helped to drive the Technology Revolution, a new quantum image algorithm on constrained least squares filtering computation was proposed from quantum mechanics, quantum information, and extremely powerful computer. A quantum image representation model is introduced to construct an image model, which is then used for image processing. Prior knowledge is employed in order to reconstruct or estimate the point spread function, and a non-degenerate estimate is obtained based on the opposite processing. The fuzzy function against noises is solved using the optimal measure of smoothness. On the constraint condition, determine the minimum criterion function and estimate the original image function. For some motion blurs and some kinds of noise pollutions, such as Gaussian noises, the proposed algorithm is able to yield better recovery results. Additionally, it should be noted that, when there is a noise attack with very low noise intensity, the model based on the constrained least squares filtering can still deliver good recovery results, with strong robustness. Subsequently, discuss the simulation analysis of the complexity of implementing quantum circuits and image filtering, and demonstrate that the algorithm has a good effect on fuzzy recovery, when the noise density is small.https://www.mdpi.com/1099-4300/22/11/1207quanta image computationquantum image algorithmimage restorationalgorithm analysis |
| spellingShingle | Shumei Wang Pengao Xu Ruicheng Song Peiyao Li Hongyang Ma Development of High Performance Quantum Image Algorithm on Constrained Least Squares Filtering Computation Entropy quanta image computation quantum image algorithm image restoration algorithm analysis |
| title | Development of High Performance Quantum Image Algorithm on Constrained Least Squares Filtering Computation |
| title_full | Development of High Performance Quantum Image Algorithm on Constrained Least Squares Filtering Computation |
| title_fullStr | Development of High Performance Quantum Image Algorithm on Constrained Least Squares Filtering Computation |
| title_full_unstemmed | Development of High Performance Quantum Image Algorithm on Constrained Least Squares Filtering Computation |
| title_short | Development of High Performance Quantum Image Algorithm on Constrained Least Squares Filtering Computation |
| title_sort | development of high performance quantum image algorithm on constrained least squares filtering computation |
| topic | quanta image computation quantum image algorithm image restoration algorithm analysis |
| url | https://www.mdpi.com/1099-4300/22/11/1207 |
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