Quantum image scaling using nearest neighbor interpolation

2014 ◽  
Vol 14 (5) ◽  
pp. 1559-1571 ◽  
Author(s):  
Nan Jiang ◽  
Luo Wang
Keyword(s):  
Nearest Neighbor ◽  
Quantum Image ◽  
Image Scaling

Quantum multidimensional color image scaling using nearest-neighbor interpolation based on the extension of FRQI

2017 ◽  
Vol 31 (17) ◽  
pp. 1750184 ◽  
Author(s):  
Ri-Gui Zhou ◽  
Canyun Tan ◽  
Ping Fan
Keyword(s):  
Nearest Neighbor ◽  
Complexity Analysis ◽  
Color Image ◽  
Scaling Up ◽  
Quantum Image ◽  
Image Scaling ◽  
Lower Complexity ◽  
Scaling Down ◽  
Classical Computer ◽  
First Time

Reviewing past researches on quantum image scaling, only 2D images are studied. And, in a quantum system, the processing speed increases exponentially since parallel computation can be realized with superposition state when compared with classical computer. Consequently, this paper proposes quantum multidimensional color image scaling based on nearest-neighbor interpolation for the first time. Firstly, flexible representation of quantum images (FRQI) is extended to multidimensional color model. Meantime, the nearest-neighbor interpolation is extended to multidimensional color image and cycle translation operation is designed to perform scaling up operation. Then, the circuits are designed for quantum multidimensional color image scaling, including scaling up and scaling down, based on the extension of FRQI. In addition, complexity analysis shows that the circuits in the paper have lower complexity. Examples and simulation experiments are given to elaborate the procedure of quantum multidimensional scaling.

Applied Technology in Multidimensional Color Image Scaling Based on NASS in a Quantum System

2014 ◽  
Vol 1046 ◽  
pp. 411-414
Author(s):  
Hai Sheng Li ◽  
Kai Song
Keyword(s):  
Quantum System ◽  
Nearest Neighbor ◽  
Quantum Computer ◽  
Color Image ◽  
Quantum Circuit ◽  
Geometric Transformation ◽  
Superposition State ◽  
Image Scaling ◽  
Applied Technology ◽  
Scaling Down

In this study, an important geometric transformation, multidimensional color image scaling based on an n-qubit normal arbitrary superposition state (NASS), is put forward. In order to reduce the complexity of implementation of image scaling in a quantum system, nearest neighbor interpolation algorithm is chosen to implement image scaling up. And the corresponding quantum circuit of implementation is proposed. Finally, we discuss measurements of the part qubits of a NASS state to realize image scaling down. The paper explores theoretical and practical aspects of image processing on a quantum computer.

Bilinear interpolation method for quantum images based on quantum Fourier transform

2018 ◽  
Vol 16 (04) ◽  
pp. 1850031 ◽  
Author(s):  
Panchi Li ◽  
Xiande Liu
Keyword(s):  
Image Processing ◽  
Fourier Transform ◽  
Nearest Neighbor ◽  
Interpolation Method ◽  
New Method ◽  
Basic Operation ◽  
Quantum Fourier Transform ◽  
Bilinear Interpolation ◽  
Image Scaling ◽  
Bicubic Interpolation

Image scaling is the basic operation that is widely used in classic image processing, including nearest-neighbor interpolation, bilinear interpolation, and bicubic interpolation. In quantum image processing (QIP), the research on image scaling is focused on nearest-neighbor interpolation, while the related research of bilinear interpolation is very rare, and that of bicubic interpolation has not been reported yet. In this study, a new method based on quantum Fourier transform (QFT) is designed for bilinear interpolation of images. Firstly, some basic functional modules are constructed, in which the new method based on QFT is adopted for the design of two core modules (i.e. addition and multiplication), and then these modules are used to design quantum circuits for the bilinear interpolation of images, including scaling-up and down. Finally, the complexity analysis of the scaling circuits based on the elementary gates is deduced. Simulation results show that the scaling image using bilinear interpolation is clearer than that using the nearest-neighbor interpolation.

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

2021 ◽  
Author(s):  
Meiyu Xu ◽  
Dayong Lu ◽  
Xiaoyun Sun
Keyword(s):  
Quantum Computation ◽  
Scaling Up ◽  
Floating Point ◽  
Geometric Transformation ◽  
Double Precision ◽  
Quantum Image ◽  
Image Scaling ◽  
Trilinear Interpolation ◽  
Point Data ◽  
Floating Point Numbers

Abstract 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.

Observation of Superlattice Dislocations on Cube Planes in Ni3Al

1973 ◽  
Vol 31 ◽  
pp. 174-175 ◽  
Author(s):  
J. M. Oblak ◽  
W. H. Rand
Keyword(s):  
Nearest Neighbor ◽  
Antiphase Boundary ◽  
Nearest Neighbors ◽  
High Energy ◽  
Cross Slip ◽  
Octahedral Plane ◽  
Trapping Mechanism ◽  
Slip Traces

The energy of an a/2 <110> shear antiphase. boundary in the Ll2 expected to be at a minimum on {100} cube planes because here strue ture is there is no violation of nearest-neighbor order. The latter however does involve the disruption of second nearest neighbors. It has been suggested that cross slip of paired a/2 <110> dislocations from octahedral onto cube planes is an important dislocation trapping mechanism in Ni3Al; furthermore, slip traces consistent with cube slip are observed above 920°K.Due to the high energy of the {111} antiphase boundary (> 200 mJ/m2), paired a/2 <110> dislocations are tightly constricted on the octahedral plane and cannot be individually resolved.

Application of Lattice Imaging Techniques to Amorphous Films

1975 ◽  
Vol 33 ◽  
pp. 8-9
Author(s):  
S. R. Herd ◽  
P. Chaudhari
Keyword(s):  
Nearest Neighbor ◽  
Random Network ◽  
Imaging Techniques ◽  
Network Models ◽  
Accurate Method ◽  
Direct Transmission ◽  
Lattice Imaging ◽  
Transmission Electron ◽  
Lattice Planes ◽  
Nearest Neighbor Distances

Electron diffraction and direct transmission have been used extensively to study the local atomic arrangement in amorphous solids and in particular Ge. Nearest neighbor distances had been calculated from E.D. profiles and the results have been interpreted in terms of the microcrystalline or the random network models. Direct transmission electron microscopy appears the most direct and accurate method to resolve this issue since the spacial resolution of the better instruments are of the order of 3Å. In particular the tilted beam interference method is used regularly to show fringes corresponding to 1.5 to 3Å lattice planes in crystals as resolution tests.

Sign in / Sign up

Export Citation Format

Share Document