Generic conversion method for various spatial domain filters in quantum image processing

Pachuau, Joseph L.; Saha, Anish Kumar

doi:10.1016/j.physa.2022.127196

Cited by 3 publications

(2 citation statements)

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“…Since the use of the traditional operators cannot have accurate profile extraction, researchers have come up with various novel ways for the image enhancement. [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31] Among them, mathematical method has been intensively used for image enhancement. One good example is shown in the application of the Hausdorff derivative, which is used for image edge extraction.…”

Section: Introductionmentioning

confidence: 99%

“…[12] The other research efforts for acquiring the image enhancement involved with the method of using wavelets. [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31] The wavelets are generally wave-like oscillatory functions. They show a recovery impact since the features acquired via the wavelets show different resolutions.…”

Section: Introductionmentioning

confidence: 99%

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Image Enhancement via Special Functions and Its Application for Near Infrared Imaging

et al. 2023

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Image enhancement is important given that it can be used to highlight the area of interest in the images. This article designs four filters via special function for realizing image enhancement. Firstly, a filter based on the exponential function is designed. When the value of the progression is even, the edge feature can be extracted. When the value of the progression is odd, sharp contrast can be obtained. Secondly, a filter is built using hyperbolic cosine and its inverse function, where a printmaking feature can be extracted. Thirdly, a filter is made via a hyperbolic secant function and its inverse. It can lead to the extraction of image edge. When the progression value is increasing, marginal effect can be found and the brightness is decreasing. Ripple morphology can be found. Fourthly, a filter is constructed through a hyperbolic sine function and its inverse, where marginal features can be extracted. Furthermore, these filters are useful for extracting the marginal features even when a high noise density of 0.9 is added to the original images. They are useful for highlighting the images acquired from near infrared imaging.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Image Enhancement via Special Functions and Its Application for Near Infrared Imaging

et al. 2023

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A probabilistic model of quantum states for classical data security

Hafiz

Hwang

2023

Front. Phys.

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Applications and Challenges of Quantum Image Processing – A Comprehensive Review

Sungheetha¹

2023

RRRJ

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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.

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Generic conversion method for various spatial domain filters in quantum image processing

Cited by 3 publications

References 21 publications

Image Enhancement via Special Functions and Its Application for Near Infrared Imaging

Image Enhancement via Special Functions and Its Application for Near Infrared Imaging

A probabilistic model of quantum states for classical data security

Applications and Challenges of Quantum Image Processing – A Comprehensive Review

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