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

Scientific Reports

Quantum implementation of the classical guided image filtering algorithm

Jiale Mu, Xiaofei Li, Xianghua Zhang, Pinghe Wang

Published Jan 2, 2025• 5 citations
Publisher

Abstract

Image filtering involves the application of window operations that perform valuable functions, such as noise removal, image enhancement, high dynamic range (HDR) compression, and so on. Guided image filtering is a new type of explicit image filter with multiple advantages. It can effectively remove noise while preserving edge details, and can be used in a variety of scenarios. Here, we report a quantum implementation of guided image filtering algorithm, based on the novel enhanced quantum representation (NEQR) model, and the corresponding quantum circuit has been designed. We find that the speed and quality of filtering are improved significantly due to the quantization, and the time complexity is reduced exponentially from $$O(2^{2q})$$ to $$O(q^2)$$ .

DOI
10.1038/s41598-024-84211-8

Related papers

Quantum implementation of the guided image filtering algorithm

Jiale Mu, Xiaofei Li, Xiang-Hua Zhang +1 more

Abstract Image filtering involves the application of window operations that perform valuable functions, such as noise removal, image enhancement, high dynamic range (HDR) compression, and so on. Guided image filtering is a new type of explicit image filter with multiple advantages. Here, we report a quantum implementation of guided image filtering algorithm, based on the novel enhanced quantum representation (NEQR) model, and the corresponding quantum circuit has been designed. We find the quality of filtering is consistent with that of the classical one and the time complexity is reduced exponentially from $O(2^{2q})$ to $O(q^2)$ due to the quantization.

•Dec 27, 2023•0 citations

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

Engin Şahin

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.

International Journal of Information Security Science•Feb 28, 2023•0 citations

Quantum Image Processing Algorithm Using Line Detection Mask Based on NEQR

Tao Li, Pengpeng Zhao, Yadong Zhou +1 more

Line detection is a fundamental technique in image processing. It can extract the required information, while the information that does not need attention can be ignored, thus reducing the amount of data. At the same time, line detection is also the basis of image segmentation and plays an important role in this process. In this paper, we implement a quantum algorithm based on a line detection mask for novel enhanced quantum representation (NEQR). We build a quantum algorithm for line detection in different directions and design a quantum circuit for line detection. The detailed module designed is also provided. On a classical computer, we simulate the quantum method, and the simulation results prove the feasibility of the quantum method. By analyzing the complexity of quantum line detection, we find that the computation complexity of the proposed method is improved compared to some similar edge detection algorithms.

Entropy•Apr 29, 2023•10 citations

Quantum image filtering and its reversible logic circuit design

Gaofeng Luo, Liang Zong, Shexiang Jiang

Quantum information processing can overcome the limitations of classical computation.Consequently, image filtering using quantum computation has become a research hotspot.Here, a quantum algorithm is presented on the basis of the classical image filtering principle to detect and cancel the noise of an image.To this end, a quantum algorithm that completes the image filtering task is proposed and implemented.The novel enhanced quantum representation of digital images is introduced.Then, four basic modules, namely, position-shifting, parallel-CNOT, parallel-swap, and compare the max, are demonstrated.Two composite modules that can be utilised to realise the reversible logic circuit of the proposed quantum algorithm are designed on the basis of these basic modules.Simulation-based experimental results show the feasibility and the capabilities of the proposed quantum image filtering scheme.In addition, our proposal has outperformed its classical counterpart and other existing quantum image filtering schemes supported by detailed theoretical analysis of the computational complexity.Thus, it can potentially be used for highly efficient image filtering in a quantum computer age.

International Journal of Embedded Systems•Jan 1, 2021•2 citations

Quantum image processing algorithm using edge extraction based on Kirsch operator

Pengao Xu, Zhenxing He, Tianhui Qiu +1 more

In this work, a quantum image processing algorithm is developed using the edge extraction method together with the Kirsch operator. In our approach, novel enhanced quantum representation (NEQR) is employed as the image representation model for processing quantum image, which generates results of edge extraction using the Kirsch operator. The proposed algorithm can perform real-time image processing with high accuracy. We carry out the design, analyses, and simulations of quantum circuits based on our approach, which shows that the image processing speed and acuracy are much better than the classical edge extraction algorithms.

Optics Express•Apr 6, 2020•51 citations

Quantum Circuit Design for Estimated Image Median Filter

S Alsaedi Alaa, Rasha Montaser, Usama A. Aburawash +1 more

Quantum image processing is a new branch of quantum computing that aims to adapt traditional image processing tasks and operations to the quantum computing field. Obtaining images with high accuracy is a very difficult task. Filters are used to enhance the efficiency of the image and modify digital images. There are many filtering techniques, such as median filtering, Laplacing filtering, Qaussin filtering, neighborhood average filtering, and box filtering. Currently, there are limitations in quantum median filtering techniques due to either incomplete noise recognition or computational complexity. We propose the design of an estimated image median filter with a noise tolerance threshold to remove salt and pepper noise, and finally, we design a quantum circuit that decreases the computational complexity to O( . Furthermore, the simulation results demonstrate that the proposed median filtering is feasible.

Journal of Advanced Research in Applied Sciences and Engineering Technology•Mar 15, 2025•0 citations
Image filtering involves the application of window operations that perform valuable functions, such as noise removal, image enhancement, high dynamic range (HDR) compression, and so on. Guided image filtering is a new type of explicit image filter with multiple advantages. It can effectively remove noise while preserving edge details, and can be used in a variety of scenarios. Here, we report a quantum implementation of guided image filtering algorithm, based on the novel enhanced quantum representation (NEQR) model, and the corresponding quantum circuit has been designed. We find that the speed and quality of filtering are improved significantly due to the quantization, and the time complexity is reduced exponentially from O(22q)O(2^{2q})O(22q) to O(q2)O(q^2)O(q2) .
Abstract Image filtering involves the application of window operations that perform valuable functions, such as noise removal, image enhancement, high dynamic range (HDR) compression, and so on. Guided image filtering is a new type of explicit image filter with multiple advantages. Here, we report a quantum implementation of guided image filtering algorithm, based on the novel enhanced quantum representation (NEQR) model, and the corresponding quantum circuit has been designed. We find the quality of filtering is consistent with that of the classical one and the time complexity is reduced exponentially from O(22q)O(2^{2q})O(22q) to O(q2)O(q^2)O(q2) due to the quantization.
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.
Line detection is a fundamental technique in image processing. It can extract the required information, while the information that does not need attention can be ignored, thus reducing the amount of data. At the same time, line detection is also the basis of image segmentation and plays an important role in this process. In this paper, we implement a quantum algorithm based on a line detection mask for novel enhanced quantum representation (NEQR). We build a quantum algorithm for line detection in different directions and design a quantum circuit for line detection. The detailed module designed is also provided. On a classical computer, we simulate the quantum method, and the simulation results prove the feasibility of the quantum method. By analyzing the complexity of quantum line detection, we find that the computation complexity of the proposed method is improved compared to some similar edge detection algorithms.
Quantum information processing can overcome the limitations of classical computation.Consequently, image filtering using quantum computation has become a research hotspot.Here, a quantum algorithm is presented on the basis of the classical image filtering principle to detect and cancel the noise of an image.To this end, a quantum algorithm that completes the image filtering task is proposed and implemented.The novel enhanced quantum representation of digital images is introduced.Then, four basic modules, namely, position-shifting, parallel-CNOT, parallel-swap, and compare the max, are demonstrated.Two composite modules that can be utilised to realise the reversible logic circuit of the proposed quantum algorithm are designed on the basis of these basic modules.Simulation-based experimental results show the feasibility and the capabilities of the proposed quantum image filtering scheme.In addition, our proposal has outperformed its classical counterpart and other existing quantum image filtering schemes supported by detailed theoretical analysis of the computational complexity.Thus, it can potentially be used for highly efficient image filtering in a quantum computer age.
In this work, a quantum image processing algorithm is developed using the edge extraction method together with the Kirsch operator. In our approach, novel enhanced quantum representation (NEQR) is employed as the image representation model for processing quantum image, which generates results of edge extraction using the Kirsch operator. The proposed algorithm can perform real-time image processing with high accuracy. We carry out the design, analyses, and simulations of quantum circuits based on our approach, which shows that the image processing speed and acuracy are much better than the classical edge extraction algorithms.
Quantum image processing is a new branch of quantum computing that aims to adapt traditional image processing tasks and operations to the quantum computing field. Obtaining images with high accuracy is a very difficult task. Filters are used to enhance the efficiency of the image and modify digital images. There are many filtering techniques, such as median filtering, Laplacing filtering, Qaussin filtering, neighborhood average filtering, and box filtering. Currently, there are limitations in quantum median filtering techniques due to either incomplete noise recognition or computational complexity. We propose the design of an estimated image median filter with a noise tolerance threshold to remove salt and pepper noise, and finally, we design a quantum circuit that decreases the computational complexity to O( . Furthermore, the simulation results demonstrate that the proposed median filtering is feasible.