Quantum image processing filters based on FRQI representation

Joseph L. Pachuau, Nongmeikapam Brajabidhu Singh, Anish Kumar Saha

Abu Kaisar Mohammad Masum, Mehran Shoushtari Moghadam, Lida Kouhalvandi +2 more

Ri‐Gui Zhou, Canyun Tan, Hou Ian

Le Quang Phuc, Dong Fangyang, Yoshinori Arai +1 more

Flexible Representation of Quantum Images (FRQI) is proposed to provide a representation of images that enables efficient preparation and image processing operators on quantum computers. FRQI captures colors and their corresponding positions in an image into a quantum state. The preparation with polynomial simple operations for FRQI turning a quantum computer from the initialized state to the FRQI state is done by using Hadamard and controlled rotation gates. The Quantum Image Compression (QIC) algorithm reduces simple operations used in the preparation process based on the minimization of Boolean equations are from same color groups of positions. Quantum image processing operators on FRQI based unitary transforms dealing with only colors, colors at some positions and the combination by the quantum Fourier transform of both colors and positions are addressed. The experiments for the storage and retrieval of images using FRQI are implemented in Matlab. The compression ratio of QIC among same color groups ranges from 6.67% to 31.62% on the Lena image.

S. P. Rajamohana, Vani Yelamali, Sakthi Mahendran K. +2 more

Effective weather forecasting for cyclones is crucial for minimizing harm to both people and the environment. Accurate estimation of tropical cyclone (TC) intensity is essential for disaster prevention. Although convolutional neural networks (CNNs) have improved this process, they often struggle to capture global spatial relationships in images. Quantum Image Processing (QIP) leverages quantum computing advantages but faces challenges such as noise and hardware limitations. This study represents the first effort to estimate tropical cyclone intensity prediction using two popular quantum image representation formats: Flexible Representation of Quantum Images (FRQI) and a Novel Enhanced Quantum Representation (NEQR), as data encoders in Quantum Convolutional Neural Networks (QCNN) utilizing INSAT 3D satellite images. By employing TCs from 2012 to 2021 as training data, the model achieved an overall mean square error (MSE) of 0.0384 for FRQI and 0.0002 for NEQR. The findings indicate that NEQR significantly outperforms FRQI in cyclone image prediction.

Joel Silos-Sánchez, Jazmín Rodríguez-Flores, Josué Román Martínez-Mireles +3 more

Quantum image processing represents a transformative approach to visual data analysis, leveraging the principles of quantum computing to overcome classical limitations. This work explores two prominent quantum image encoding methods: FRQI (Flexible Representation of Quantum Images) and NEQR (Novel Enhanced Quantum Representation). FRQI excels in qubit efficiency, making it suitable for hardware implementation, while NEQR offers superior precision in pixel intensity representation, ideal for complex image processing tasks. We detail the implementation of these algorithms, including preprocessing, quantum circuit design, and simulation, using platforms like Qiskit. The study highlights the potential of quantum image processing in fields such as medicine, industry, and environmental monitoring, while addressing challenges like qubit limitations and noise sensitivity. This research contributes to advancing quantum computing applications, paving the way for innovative and sustainable technological solutions.