A framework for objective image quality measures based on intuitionistic fuzzy sets

Hassaballah, M.; Ghareeb, A.

doi:10.1016/j.asoc.2017.03.046

Cited by 31 publications

(5 citation statements)

References 47 publications

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“…This theory is close to human real thinking and decision making [39]. Hassaballah et al [40] constructed an image quality evaluation mechanism based on fuzzy sets. Ansari et al [41] combined the fuzzy set and entropy to extract the target boundary in an image.…”

Section: Figure 2: High Dynamic Star Imagementioning

confidence: 78%

An Edge Computing Algorithm Based on Multi-Level Star Sensor Cloud

Ren¹,

Qiu²,

Cheng³

2023

Computer Modeling in Engineering &Amp; Sciences

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Star sensors are an important means of autonomous navigation and access to space information for satellites. They have been widely deployed in the aerospace field. To satisfy the requirements for high resolution, timeliness, and confidentiality of star images, we propose an edge computing algorithm based on the star sensor cloud. Multiple sensors cooperate with each other to form a sensor cloud, which in turn extends the performance of a single sensor. The research on the data obtained by the star sensor has very important research and application values. First, a star point extraction model is proposed based on the fuzzy set model by analyzing the star image composition, which can reduce the amount of data computation. Then, a mapping model between content and space is constructed to achieve low-rank image representation and efficient computation. Finally, the data collected by the wireless sensor is delivered to the edge server, and a different method is used to achieve privacy protection. Only a small amount of core data is stored in edge servers and local servers, and other data is transmitted to the cloud. Experiments show that the proposed algorithm can effectively reduce the cost of communication and storage, and has strong privacy.

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Section: Figure 2: High Dynamic Star Imagementioning

confidence: 78%

An Edge Computing Algorithm Based on Multi-Level Star Sensor Cloud

Ren¹,

Qiu²,

Cheng³

2023

Computer Modeling in Engineering &Amp; Sciences

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“…To validate the effectiveness and quality of image enhancement of the proposed scheme, we have introduced some well-known image quality measurement matrices such as mean square error, mean absolute error (MAE), index of fuzziness, and so on. 23,31,32 In this article, MAE, 32 linear index of fuzziness (LIF), 32 Weber-law-based contrast measure (EMEE), 31 Michelson law measure of enhancement (AME), 10 Michelson law measure of enhancement by entropy (AMEE), 31 universal quality index (UQI), 23 structural similarity (SSIM), 27,31 fuzzy quality index (FSQI), 27 and intuitionistic fuzzy quality index (IFSQI) 23,27 have been utilized to evaluate the improved contrast quality as well as the visual clarity of the enhanced images. As mentioned above, all values of each metric of larger index indicate better enhancement result and clear visual quality except MAE.…”

Section: Performance Metrics and Baseline Methodsmentioning

confidence: 99%

“…The contrast enhancement methods based on type-I FSs still contain some artifacts with less gray levels and low contrast values. 22,23 IFSs have fascinated much attention in image processing field in recent years. 21,24 Recently, the application of the intuitionistic fuzzy special set (IFSS) in different area was introduced in Reference 24.…”

Section: Introductionmentioning

confidence: 99%

Development of intuitionistic fuzzy special embedded convolutional neural network for mammography enhancement

Ghosh

Biswas

Ghosh³

2020

Computational Intelligence

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This article proposes a novel mammogram enhancement approach using adaptive intuitionistic fuzzy special set (IFSS) with deep convolutional neural network (called MECNNIFS) for visual interpretation of mammography lesions, lumps, and abnormal cells in low-dose X-ray images. The proposed MECNNIFS scheme utilizes the membership grade modification by IFSS on low-dose X-ray images (mammography). The suggested model attempts to increase the underexposed and abnormal structural regions such as breast lesions, lumps, and nodules on the mammogram. The proposed algorithm initially separates mammograms using convolutional neural networks (CNNs) into foreground and background areas and then fuzzifies the image by intuitionistic fuzzy set theory. Low-level features of a mammogram of the adjacent part are integrated with CNN in pixel classification during the separation task stage to improve the performance. Hyperbolic regularization and hesitant score have been applied on fuzzy plane to quantify the uncertainty and fuzziness in spatial domain for the proposed contrast enhancement. Finally, an enhanced mammogram is acquired through the process of defuzzification. The results show better quality and performance for improvement of contrast and Mammography Lesion Detection using IFSS and CNN.

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“…Due to strong data-inclusive features, IFS and IVIFS have been extended in distinct regions, including bipolar soft sets ( Mahmood, 2020 ), analysis of image quality under measures ( Hassaballah & Ghareeb, 2017 ), decision-making framework ( Gao et al, 2021 ; Zeng, Hu & Llopis-Albert, 2023 ), distance and similarity measures ( Garg & Rani, 2021 ; Peng, Xiaohe & Jianbo, 2021 ), hybrid variable approach ( Liu et al, 2021 ; Xue, Deng & Garg, 2021 ; Zhang et al, 2022 ), construction of shadowed sets ( Yang & Yao, 2021 ), time-series mapping ( Bas, Yolcu & Egrioglu, 2021 ), transportation problem ( Bharati, 2021 ), and combined compromise solution approach ( Alrasheedi et al, 2021 ; Su et al, 2023 ).…”

Section: Literature Reviewmentioning

confidence: 99%

WASPAS method and Aczel-Alsina aggregation operators for managing complex interval-valued intuitionistic fuzzy information and their applications in decision-making

Fang

Mahmood

Ali

et al. 2023

PeerJ Computer Science

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Aczel-Alsina t-norm and t-conorm are a valuable and feasible technique to manage ambiguous and inconsistent information because of their dominant characteristics of broad parameter values. The main theme of this analysis is to explore Aczel-Alsina operational laws in the presence of the complex interval-valued intuitionistic fuzzy (CIVIF) set theory. Furthermore, we derive the theory of aggregation frameworks based on Aczel-Alsina operational laws for managing the theory of CIVIF information. The CIVIF Aczel-Alsina weighted averaging (CIVIFAAWA), CIVIF Aczel-Alsina ordered weighted averaging (CIVIFAAOWA), CIVIF Aczel-Alsina hybrid averaging (CIVIFAAHA), CIVIF Aczel-Alsina weighted geometric (CIVIFAAWG), CIVIF Aczel-Alsina ordered weighted geometric (CIVIFAAOWG) and CIVIF Aczel-Alsina hybrid geometric (CIVIFAAHG) operators are proposed, and their well-known properties and particular cases are also detailly derived. Further, we derive the theory of the WASPAS method for CIVIF information and evaluate their positive and negative aspects. Additionally, we demonstrate the multi-attribute decision-making (MADM) strategy under the invented works. Finally, we express the supremacy and dominancy of the invented methods with the help of sensitive analysis and geometrical shown of the explored works.

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A framework for objective image quality measures based on intuitionistic fuzzy sets

Cited by 31 publications

References 47 publications

An Edge Computing Algorithm Based on Multi-Level Star Sensor Cloud

An Edge Computing Algorithm Based on Multi-Level Star Sensor Cloud

Development of intuitionistic fuzzy special embedded convolutional neural network for mammography enhancement

WASPAS method and Aczel-Alsina aggregation operators for managing complex interval-valued intuitionistic fuzzy information and their applications in decision-making

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