Bilinear interpolation method for quantum images based on quantum Fourier transform

Li, Panchi; Liu, Xiande

doi:10.1142/s0219749918500314

Cited by 36 publications

(29 citation statements)

References 26 publications

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“…Therefore, we summarized and analyzed the existing image interpolation methods. The current image interpolation methods mainly include nearest neighbor interpolation [54], bilinear interpolation [55], and cubic convolution interpolation [56]. The advantage of the nearest neighbor interpolation method is that the calculation is very small and the operation speed is fast.…”

Section: Discussionmentioning

confidence: 99%

Fusing China GF-5 Hyperspectral Data with GF-1, GF-2 and Sentinel-2A Multispectral Data: Which Methods Should Be Used?

et al. 2020

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The China GaoFen-5 (GF-5) satellite sensor, which was launched in 2018, collects hyperspectral data with 330 spectral bands, a 30 m spatial resolution, and 60 km swath width. Its competitive advantages compared to other on-orbit or planned sensors are its number of bands, spectral resolution, and swath width. Unfortunately, its applications may be undermined by its relatively low spatial resolution. Therefore, the data fusion of GF-5 with high spatial resolution multispectral data is required to further enhance its spatial resolution while preserving its spectral fidelity. This paper conducted a comprehensive evaluation study of fusing GF-5 hyperspectral data with three typical multispectral data sources (i.e., GF-1, GF-2 and Sentinel-2A (S2A)), based on quantitative metrics, classification accuracy, and computational efficiency. Datasets on three study areas of China were utilized to design numerous experiments, and the performances of nine state-of-the-art fusion methods were compared. Experimental results show that LANARAS (this method was proposed by lanaras et al.), Adaptive Gram–Schmidt (GSA), and modulation transfer function (MTF)-generalized Laplacian pyramid (GLP) methods are more suitable for fusing GF-5 with GF-1 data, MTF-GLP and GSA methods are recommended for fusing GF-5 with GF-2 data, and GSA and smoothing filtered-based intensity modulation (SFIM) can be used to fuse GF-5 with S2A data.

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Section: Discussionmentioning

confidence: 99%

Fusing China GF-5 Hyperspectral Data with GF-1, GF-2 and Sentinel-2A Multispectral Data: Which Methods Should Be Used?

et al. 2020

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“…In [24], Li proposed a new method for the design of two core modules (i.e. addition and multiplication) based on QFT.…”

Section: Adder and M Ultiplier Modulesmentioning

confidence: 99%

“…Adder and Multiplier modules based on floating-point number are given in [28]. We will benefit from the circuits given by [24,28] in our quantum addition and multiplication circuits based on QFT. In this paper, we design the addition and multiplication based on QFT (Q-Adder and Q-M ultiplier) operations combine [24] with [28], and the quantum circuits are depicted in Figures 12 and 13.…”

Section: Floating-point Addition and Multiplication Based On Qftmentioning

confidence: 99%

“…We will benefit from the circuits given by [24,28] in our quantum addition and multiplication circuits based on QFT. In this paper, we design the addition and multiplication based on QFT (Q-Adder and Q-M ultiplier) operations combine [24] with [28], and the quantum circuits are depicted in Figures 12 and 13. In order to have the same number of bits of the two floating-point numbers that are multiplied, we designed the Converter module for converting fixed-point numbers to floating-point numbers in the design scheme of quantum scaling for 3-D floating-point data, and the quantum circuit is depicted in Figure 14.…”

Section: Floating-point Addition and Multiplication Based On Qftmentioning

confidence: 99%

“…In [23], Zhou et al proposed the bilinear interpolation method for NEQR and given the corresponding quantum realization circuits. In [24], Li and Liu designed quantum image scaling using bilinear interpolation method based on QFT. In [25], Zhou et al given quantum image scaling based on bilinear interpolation with arbitrary scaling ratio.…”

Section: Introductionmentioning

confidence: 99%

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Scaling Up and Down of 3-D Floating-point Data in Quantum Computation

Sun

2021

Preprint

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In the past few decades, quantum computation has become increasingly attractivedue to its remarkable performance. Quantum image scaling is considered a common geometric transformation in quantum image processing, however, the quantum floating-point data version of which does not exist. Is there a corresponding scaling for 2-D and 3-D floating-point data? The answer is yes.In this paper, we present quantum scaling up and down scheme for floating-point data by using trilinear interpolation method in 3-D space. This scheme offers better performance (in terms of the precision of floating-point numbers) for realizing the quantum floating-point algorithms compared to previously classical approaches. The Converter module we proposed can solve the conversion of fixed-point numbers to floating-point numbers of arbitrary size data with p + q qubits based on IEEE-754 format, instead of 32-bit single-precision, 64-bit double precision or 128-bit extended-precision. Usually, we use nearest neighbor interpolation and bilinear interpolation to achieve quantum image scaling algorithms, which are not applicable in high-dimensional space. This paper proposes trilinear interpolation of floating-point numbers in 3-D space to achieve quantum algorithms of scaling up and down for 3-D floating-point data. Finally, the circuits of quantum scaling up and down for 3-D floating-point data are designed.

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Quantum Color Image Scaling on QIRHSI Model

Chen

Song

2021

Communications in Computer and Information Science

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Bilinear interpolation method for quantum images based on quantum Fourier transform

Cited by 36 publications

References 26 publications

Fusing China GF-5 Hyperspectral Data with GF-1, GF-2 and Sentinel-2A Multispectral Data: Which Methods Should Be Used?

Fusing China GF-5 Hyperspectral Data with GF-1, GF-2 and Sentinel-2A Multispectral Data: Which Methods Should Be Used?

Scaling Up and Down of 3-D Floating-point Data in Quantum Computation

Quantum Color Image Scaling on QIRHSI Model

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