Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Quantum Information Processing

Quantum realization of the nearest neighbor value interpolation method for INEQR

Ri‐Gui Zhou, WenWen Hu, Gaofeng Luo, XingAo Liu, Ping Fan

Published May 22, 2018• 38 citations
Publisher
DOI
10.1007/s11128-018-1921-y

Related papers

Quantum realization of the nearest-neighbor interpolation method for FRQI and NEQR

Jianzhi Sang, Shen Wang, Xiamu Niu

Quantum Information Processing•Nov 13, 2015•56 citations

Quantum image scaling using nearest neighbor interpolation

Nan Jiang, Luo Wang

Quantum Information Processing•Sep 29, 2014•239 citations

Quantum image scaling up based on nearest-neighbor interpolation with integer scaling ratio

Nan Jiang, Jian Wang, Yue Mu

Quantum Information Processing•Aug 28, 2015•155 citations

Implementing Bilinear Interpolation on Quantum Images

Fei Yan, Shan-Shan Zhao, Salvador Elías Venegas-Andraca

In this paper, we present an interpolation scheme for FRQI images based on bilinear interpolation. To accomplish this, we formulated several quantum modules, i.e., assignment module, increment module, and quarter module, and suffused them into our proposed quantum image interpolation circuit. The concrete quantum circuits to accomplish up-scaling and down-scaling based on bilinear for FRQI images are designed and the network complexities of them are analyzed. Finally, to validate the proposed method, simulation experiments to enlarge and reduce the test images are executed, whose results are compared with the nearest neighbor interpolation for FRQI images. The up-scaled images by using proposed interpolation algorithm achieve satisfactory results, and both PSNR and SSIM values are better than those of the nearest neighbor method.

arXiv:2010.10254•Oct 20, 2020•0 citations

An Improved Quantum Nearest-Neighbor Algorithm

Ying Zhang, Feng Bao, Wei Jia +1 more

Advances in intelligent systems and computing•Jul 1, 2020•3 citations

A Novel Quantum Generalized Neighbor Interpolation Design for Image Transformations

Israel Koiku, Edgard Muñoz‐Coreas

In this work, we propose a novel Quantum Generalized Neighbor Interpolation (QGNI) circuit which can be used to implement Nearest Neighbor Interpolation (NNI) as a special case of its operations. Nearest neighbor interpolation (NNI) is used in image processing tasks such as image rotation. The proposed quantum circuit takes as input (i) the perpendicular distance between a transformed pixel value and one of the four adjacent integral coordinate locations and (ii) a weighting factor which determines which of the four adjacent integral coordinate locations a transformed pixel value is projected to. corresponding T gate cost is provided.

IEEE Computer Society Annual Symposium on VLSI•Jul 1, 2024•0 citations
In this paper, we present an interpolation scheme for FRQI images based on bilinear interpolation. To accomplish this, we formulated several quantum modules, i.e., assignment module, increment module, and quarter module, and suffused them into our proposed quantum image interpolation circuit. The concrete quantum circuits to accomplish up-scaling and down-scaling based on bilinear for FRQI images are designed and the network complexities of them are analyzed. Finally, to validate the proposed method, simulation experiments to enlarge and reduce the test images are executed, whose results are compared with the nearest neighbor interpolation for FRQI images. The up-scaled images by using proposed interpolation algorithm achieve satisfactory results, and both PSNR and SSIM values are better than those of the nearest neighbor method.
In this work, we propose a novel Quantum Generalized Neighbor Interpolation (QGNI) circuit which can be used to implement Nearest Neighbor Interpolation (NNI) as a special case of its operations. Nearest neighbor interpolation (NNI) is used in image processing tasks such as image rotation. The proposed quantum circuit takes as input (i) the perpendicular distance between a transformed pixel value and one of the four adjacent integral coordinate locations and (ii) a weighting factor which determines which of the four adjacent integral coordinate locations a transformed pixel value is projected to. corresponding T gate cost is provided.