Applications and Challenges of Quantum Image Processing – A Comprehensive Review

Abstract

The tremendous parallel processing capability of quantum computers allow quantum image processing, a multidisciplinary field combining image processing and quantum computing, to expand the potential outcomes for image processing. The problem of quantum computation is to create effective quantum algorithms since quantum computers need extremely effective algorithms than classical algorithm. Additionally, information storage, communication, and computing power are increasing in relevance with the number and importance of processing digital images. Some of these issues might be resolved by encoding the visual data in quantum-mechanical systems as opposed to classical methods and by switching from classical to quantum information processing. Moreover, the quantum image processing is analysed based on the recent research works and is summarized. This study projects the application of quantum image processing and challenges.

Advancements and Applications of Quantum Computing in Robotics

“…Here are some such quantum picture representations: The following are various models and representations used in quantum image processing: new quantum representation model of color digital images (QRCI) [18], entangled Image [19], bitplane representation of quantum images (BRQI) [20], flexible representation for quantum images (FRQI) [21], novel quantum representation of color digital images (NCQI) [22], enhanced quantum representation (NEQR) [23], generalized model of NEQR (GNEQR) [24], normal arbitrary quantum superposition state (NASS) [25], Improved NEQR (INEQR) [26], multi-channel representation of quantum image (MCRQI) [27], multi-channel quantum images (MCQI) [28], Caraiman's quantum Image representation (CQIR) [29], simple quantum representation of infrared images (SQR) [30], and quantum log-polar images (QUALPI) [31].Various models exist for representing images based on quantum principles, including the Double Quantum Color Images Representation Model (DRQCI) [32], Quantum Representation of Multi Wavelength Images (QRMW) [33], Quantum Representation Model for Multiple Images (QRMMI) [34], Optimized Quantum Representation for Color Digital Images (OCQR) [35], Improved FRQI Model (FRQCI) [36], Order-Encoded Quantum Image Model (OQIM) [37], Quantum Block Image Representation (QBIR) [38], Improved FRQI, and Digital RGB Multi-Channel Representation for Quantum Colored Images (QMCR) [39], among others. Quantum imagestore color pixel's locations and representations in distinct manners, resulting in variations in algorithmic complexity and applications of image processing.…”