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Journal of Advanced Research in Applied Sciences and Engineering Technology

Quantum Circuit Design for Estimated Image Median Filter

S Alsaedi Alaa, Rasha Montaser, Usama A. Aburawash, Nermeen Kashief

Published Mar 15, 2025• 0 citations
Publisher

Abstract

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.

DOI
10.37934/araset.62.4.162174

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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.
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Abstract In this new computing paradigm, named quantum computing, researchers from all over the world are taking their first steps in designing quantum circuits for image processing, through a difficult process of knowledge transfer. This effort is named quantum image processing, an emerging research field pushed by powerful parallel computing capabilities of quantum computers. This work goes in this direction and proposes the challenging development of a powerful method of image denoising, such as the total variation (TV) model, in a quantum environment. The proposed quantum TV is described and its sub-components are analysed. Despite the natural limitations of the current capabilities of quantum devices, the experimental results show a competitive denoising performance compared to the classical variational TV counterpart.
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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.