Quantum image mid-point filter

Ali, Abdalla Essam; Abdel-Galil, Hala; Mohamed, Soha

doi:10.1007/s11128-020-02738-x

Cited by 7 publications

(10 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The algorithm in a study by Ali et al. 30 is a midpoint filtering method, which only requires the maximum and minimum values and then finds the median of these two numbers. It also requires the cycle shift modules to obtain the neighboring pixels and the complexity of this module is O

.…”

Section: Resultsmentioning

confidence: 99%

“…Based on quantum image representation models, many quantum image processing algorithms have been proposed. Such as geometric transformation of quantum image, 10 , 11 quantum image steganography based on least significant bit (LSB), 12 feature extraction of quantum image, 8 quantum image scaling, 13 quantum image matching, 14 quantum image edge detection, 15 , 16 , 17 , 18 , 19 , 20 quantum image segmentation, 21 , 22 , 23 , 24 , 25 , 26 quantum image filtering, 27 , 28 , 29 , 30 quantum image recognition and classification, 31 , 32 , 33 , 34 etc.…”

Section: Introductionmentioning

confidence: 99%

“…In 2020, Essam et al. 30 proposed a new midpoint filtering method, which is a filter based order statistical filtering. They adopted modular design to design filtering quantum circuits and reduce the number of auxiliary qubits.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A quantum synthetic aperture radar image denoising algorithm based on grayscale morphology

Wang,

Liu,

Meng

et al. 2024

iScience

View full text Add to dashboard Cite

.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A quantum synthetic aperture radar image denoising algorithm based on grayscale morphology

Wang,

Liu,

Meng

et al. 2024

iScience

View full text Add to dashboard Cite

“…On the basis of the quantum image representation, many quantum image processing algorithms have been proposed, such as geometric transformation, [10] image scaling, [8,9,[11][12][13][14][15][16][17][18] edge detection, [19] image filtering, [20] image watermarking, [21] feature extraction, [22] and image segmentation. [23] Among them, image scaling is one of the most commonly used and basic operations in image processing, which can adjust the size of digital image.…”

Section: Introductionmentioning

confidence: 99%

Integer multiple quantum image scaling based on NEQR and bicubic interpolation

Cai 蔡,

Zhou 周,

Luo 罗

et al. 2024

Chinese Phys. B

View full text Add to dashboard Cite

As a branch of quantum image processing, quantum image scaling has been widely studied. However, most of the existing quantum image scaling algorithms are based on nearest-neighbor interpolation and bilinear interpolation, the quantum version of bicubic interpolation has not yet been proposed. In this paper, we present the first quantum image scaling scheme for bicubic interpolation based on the novel enhanced quantum representation (NEQR). Our scheme can realize synchronous enlargement and reduction of 2 n ×2 n size images by integer multiples. Firstly, the image is represented using NEQR and the original image coordinates are obtained through multiple CNOT modules. Then, 16 neighborhood pixels are obtained by quantum operation circuits, and the corresponding weights of these pixels are calculated by quantum arithmetic modules. Finally, instead of a classical convolution operation, a quantum matrix operation is used to realize the sum of convolution of these pixels. Through simulation experiments and complexity analysis, we demonstrate that our scheme achieves exponential speedup over the classical bicubic interpolation algorithm, and has better effect than the quantum version of bilinear interpolation.

show abstract

“…Quantum circuits are designed with some auxiliary modules for the arithmetic operations and the weighted averaging filter of size 3 × 3 to be used in noise removal applications on the images. Quantum superposition is not used in this study and therefore many ancillary qubits are required [18]. Li et al [17] performed the quantum median filter in the spatial domain, then improved the method in terms of noise removal and complexity in Ref.…”

Section: Introductionmentioning

confidence: 99%

Realization of quantum mean filters with different sized on NEQR quantum images by QFT based operations

Şahin

2023

International Journal of Information Security Science

View full text Add to dashboard Cite

For some image processing algorithms such as edge detection and segmentation, filtering in the spatial domain is an important pre-processing to improve the quality of the image by removing the noise in the images. The time and memory requirements for processing also increase as the size of the images increases. Besides, excessive blurring of the image during noise removal operation can lead to excessive deterioration of image quality. Therefore, the balance between noise reduction and blur needs to be well adjusted. In this paper, a new quantum method for noise reduction on quantum images using QFT-based arithmetic operators is proposed and quantum circuits are designed. Mean filtering operators of different sizes are used to remove noise. First, the classical image is represented by the gray scale quantum image model, the novel enhanced quantum representation (NEQR) model. A quantum method is proposed and circuits are presented for the realization of Mean filters of different sizes on the basis of addition and division operations. Finally, the performance of the method is evaluated by presenting the circuit complexity of the method and the experimental results. In the proposed method, it is aimed to use less resources and reduce the circuit complexity. The optimal filtering operator size is investigated for the balance between noise reduction and image blur.

show abstract

Quantum image mid-point filter

Cited by 7 publications

References 28 publications

A quantum synthetic aperture radar image denoising algorithm based on grayscale morphology

A quantum synthetic aperture radar image denoising algorithm based on grayscale morphology

Integer multiple quantum image scaling based on NEQR and bicubic interpolation

Realization of quantum mean filters with different sized on NEQR quantum images by QFT based operations

Contact Info

Product

Resources

About