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Mean filter based on quantum mechanics

Peizhen Wang

Published Jan 1, 2010• 0 citations
OpenAlex

Abstract

Quantum computation and quantum information is an emerging cross-disciplinary research field and includes some subjects,such as physics,computer sciences,mathematics and information sciences and so on.By employing quantum information theory,we present an adaptive mean filter program based on quantum mechanics.In the proposed mean filter,the moved window image pixels are denoted by quantum superposition states.With the action of measurement operators generated by the characteristics of pixels on the image window to adjust the weights of the mean operator window,then the filter based on quantum mechanics is proposed.Computer simulation shows that the proposed mean filter is more effective than the traditional one.

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Quantum computation and quantum information is an emerging cross-disciplinary research field and includes some subjects,such as physics,computer sciences,mathematics and information sciences and so on.By employing quantum information theory,we present an adaptive mean filter program based on quantum mechanics.In the proposed mean filter,the moved window image pixels are denoted by quantum superposition states.With the action of measurement operators generated by the characteristics of pixels on the image window to adjust the weights of the mean operator window,then the filter based on quantum mechanics is proposed.Computer simulation shows that the proposed mean filter is more effective than the traditional one.
A new approach based on quantum theory for the decomposition of image was proposed in this paper.An image can be decomposed into a series of characteristic sub-images according to the relation between the pixels built by means of a superposition of states in quantum mechanics in this approach.The characteristics of these sub-images were analyzed and a rule used to generate an operator for the detection of image edge was given on the basis of this.A new algorithm for image edge detection was proposed with the rule.The computer simulation results show the effectiveness of the rule and the superiority of the algorithm proposed here.
Quantum image processing is paramount in image processing-based research areas nowadays due to the less computational time. This paper presents the quantum mechanic-based image denoising model. Also, This article discusses the quantum boolean mean filter and new quantum-boolean image denoising that are connected to computational basis states. This paper describes basic quantum information processing principles, new and better image representation approaches, quantum image processing implementation problems, quantum boolean mean filtering approach for image denoising observation using appropriate matrice, and finally, a work proposal for future work and conclusion. A comparison table of different classification techniques is also included in the paper.
Quantum computation and quantum information is a new research field and interdiscipline of physics,mathematics,computer and information sciences.It is a wonderful combination of the theory of quantum mechanics and classical computation.It has been concerned by many national governments and research groups.Algorithm is the kernel of classical computation as well known,and this situation is the same to quantum computation.Currently,the most representative algorithms are only two.One is Shor's algorithm.The other is Grover's algorithm.The fast computation of set operation is very important,and it is the base of many aspects of sciences and techniques,such as database operation,image processing,signal processing.But,for the set with high-dimension unsorted vectors,the classical electronic computer can do nothing for the requirement of fast computation.Therefore,the new computation principle and new algorithm will be needed for set operations.Quantum image processing is a progress that uses quantum computer to process image and wants to get a better result than that of the electronic computer.It is a new born topic,which will be a hot research topic in the future.This paper presents a comprehensive statementabout quantum algorithm,quantum set operation,quantum image processing and quantum Hopfield neural network.
Physics and computer science have a long tradition of cross-fertilization. One of the latest outcomes of this mutually beneficial relationship is quantum information science, which comprises the study of information processing tasks that can be accomplished using quantum mechanical systems. Quantum Image Processing (QIMP) is an emergent field of quantum information science whose main goal is to strengthen our capacity for storing, processing, and retrieving visual information from images and video either by transitioning from digital to quantum paradigms or by complementing digital imaging with quantum techniques. The expectation is that harnessing the properties of quantum mechanical systems in QIMP will result in the realization of advanced technologies that will outperform, enhance or complement existing and upcoming digital technologies for image and video processing tasks.
In order to solve the high complexity of classical image processing, a quantum scheme for image sharpness estimation based on the Laplacian operator is proposed. The mean of grayscale gradients of all pixels is regarded as the sharpness estimation metric. A new quantum image representation model is presented by extending the Novel Enhanced Quantum Representation (NEQR) model, which is greatly useful for quantum image convolution. In quantum platforms, it has been shown that the mean calculation of numbers is rather difficult because the numbers are stored in a quantum superposition state. In order to solve this problem, we put forward an algorithm which essential idea is cyclically shifting the superposition state and iteratively calculating the mean of the new one and the original state. The mean can be obtained from the superposition state by only one quantum measurement. By analyzing the space complexity and time complexity, the scheme is far superior to classical ones in terms of resource consumption and execution speed. In addition, the results of simulation experiments show that for noiseless images, the performance of the scheme is consistent with subjective visual perception of images sharpness.
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.