研究目的
Investigating the quantum image edge extraction based on classical Sobel operator for NEQR to resolve the real-time problem of image edge extraction in practice image processing.
研究成果
The proposed quantum image edge extraction algorithm based on the classical Sobel operator for NEQR can significantly reduce computational complexity, offering an exponential speedup over classical and some existing quantum edge extraction algorithms. This advancement has the potential to resolve the real-time problem in image edge extraction, making it more efficient for practical applications.
研究不足
The study focuses on the NEQR model and may not be directly applicable to other quantum image representations. The real-time performance improvement is theoretical and depends on the practical implementation of quantum computing technologies.
1:Experimental Design and Method Selection:
The methodology involves designing a quantum image edge extraction algorithm based on the classical Sobel operator for the novel enhanced quantum representation (NEQR). The NEQR model utilizes quantum mechanics' properties to store image pixels in a superposition state, enabling parallel computation of gradients for all pixels simultaneously.
2:Sample Selection and Data Sources:
The study uses digital images quantized into NEQR quantum images for processing.
3:List of Experimental Equipment and Materials:
Quantum circuits and modules such as parallel adder (PA), parallel subtractor (PS), double operation (DO), X-Shift and Y-Shift transformations, and quantum unitary operators US and UT are employed.
4:Experimental Procedures and Operational Workflow:
The workflow includes quantizing the digital image into a NEQR quantum image, applying shift transformations to obtain shifted image sets, calculating each pixel's gradient using Sobel mask, and extracting edges through threshold operation UT.
5:Data Analysis Methods:
The computational complexity of the proposed quantum edge extraction algorithm is analyzed and compared with classical and existing quantum edge extraction algorithms.
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