Quantum Image Processing

Maged Marghany

The keystone question is: what is meant by quantum image processing. Prior to answering this question, the basis of quantum computing must be understood. In fact, many academic institutions deal with mineral image processing as software that facilitates obtaining results without a perfect understanding of how the image processing algorithms are operating. This chapter demonstrates the principle of quantum image processing. The discrimination between bit and qubit is addressed. Indeed, the keystone of quantum image processing is based on using qubit as discussed clearly in this chapter. Some basic quantum image processing such as quantum image storage, quantum image colour transfer and novel enhanced quantum image representation (NEQR) are introduced. These principles of quantum image processing are the cornerstone in dealing with quantum image processing tool for mineral exploration in remote sensing data.

Woon Siong Gan

Jie Su, Xuchao Guo, Chengqi Liu +1 more

Quantum image processing will be an important issue in the age of quantum computers. Quantum image processing generally includes quantum image representations, quantum image processing algorithms, and quantum image measurement. Among them, quantum image representation is a very important part of quantum image processing. Therefore, it is of great significance for quantum image processing to study the representation of quantum images. First, we elaborate on the main quantum image representations and analyze, compare and summarize them. This paper focuses on the definition of these quantum image representations the preparation of quantum images and the number of quantum bits required as well as the computational complexity. Then, this paper analyzes, compares and summarizes the similarities and differences between these quantum image representations. Finally, the challenges and future development in the field of quantum image processing are summarized and forecasted. This work will help researchers to understand the advances related to quantum image representations in the field of quantum image processing and is therefore of some reference value.

J. Jennifer Ranjani

Sanjay Chakraborty, Lopamudra Dey

Dr. Aslam Suriya and Dr. S. B. Kishor Ashwini Somnathe

The arena of image processing is witnessing innovative and technologically advanced approaches which have significantly improved the process by multiple parameters. Due to its application in multiple sectors such as medicine, surveillance, satellite communication, automated vehicles etc., image processing has become one of the most sought-after technologies in computers. The motivation of this paper is to offer an in-depth contrast as well as analysis between Digital Image Processing and Quantum Image Processing concepts. This paper describes the elementary concepts of Digital Image Processing (DIP), core components of DIP including image filtering, padding, thresholding and connected components and fundamental steps of DIP. The process of sampling and quantization as well as the relationship between pixels is explained. A major area of focus in this paper is QIP (Quantum-Image-Processing) as well as the principles of quantum computing such as superposition, entanglement, and interference. Comparison between classical bits and qubits is also described. The paper elaborates Genetic Algorithms used for DIP and QIP algorithms such as FRQI(Flexible-Representation-of-Quantum-Images) and NEQR(Novel-Enhanced-Quantum-Representation). The paper concludes with key observations made during the study of DIP and QIP techniques and a fundamental understanding of suitability of the above-mentioned techniques depending on the problem statement.