Quantum-Inspired Edge Detection Algorithms Implementation using New Dynamic Visual Data Representation and Short-Length Convolution Computation

• URL: http://arxiv.org/abs/2210.17490v1
• Date: Mon, 31 Oct 2022 17:13:27 GMT
• Title: Quantum-Inspired Edge Detection Algorithms Implementation using New Dynamic Visual Data Representation and Short-Length Convolution Computation
• Authors: Artyom M. Grigoryan, Sos S. Agaian, Karen Panetta
• Abstract summary: This paper studies a new paired transform-based quantum representation and computation of one-dimensional and 2-D signals convolutions and gradients. The new data representation is demonstrated on multiple illustrative examples for quantum edge detection, gradients, and convolution.
• Score: 6.950510860295866
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As the availability of imagery data continues to swell, so do the demands on transmission, storage and processing power. Processing requirements to handle this plethora of data is quickly outpacing the utility of conventional processing techniques. Transitioning to quantum processing and algorithms that offer promising efficiencies over conventional methods can address some of these issues. However, to make this transformation possible, fundamental issues of implementing real time Quantum algorithms must be overcome for crucial processes needed for intelligent analysis applications. For example, consider edge detection tasks which require time-consuming acquisition processes and are further hindered by the complexity of the devices used thus limiting feasibility for implementation in real-time applications. Convolution is another example of an operation that is essential for signal and image processing applications, where the mathematical operations consist of an intelligent mixture of multiplication and addition that require considerable computational resources. This paper studies a new paired transform-based quantum representation and computation of one-dimensional and 2-D signals convolutions and gradients. A new visual data representation is defined to simplify convolution calculations making it feasible to parallelize convolution and gradient operations for more efficient performance. The new data representation is demonstrated on multiple illustrative examples for quantum edge detection, gradients, and convolution. Furthermore, the efficiency of the proposed approach is shown on real-world images.

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