Regularized Multiframe Super-Resolution Image Reconstruction Using Linear and Nonlinear Filters

Khattab, Mahmoud M.; Zeki, Akram M.; Alwan, Ali A.; Bouallègue, Belgacem; Matter, Safaa S.; Ahmed, Abdelmoty M.

doi:10.1155/2021/8309910

Cited by 7 publications

(5 citation statements)

References 38 publications

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“…The second stage of our system involves preprocessing the data to prepare it for further processing [25][26][27][28][29][30][31][32]. To ensure robustness, separate images are taken of multiple signers with different hand sizes and complex backgrounds.…”

Section: Image Preprocessing and Hand Detectionmentioning

confidence: 99%

Alphabet Recognition in Arabic Sign Language: A Machine Learning Perspective

Khattab,

Zeki,

Matter

et al. 2024

مجلة کلية الآداب بقنا

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Pattern recognition in human-computer interaction systems has gained significant attention in recent years, particularly in computer vision and machine learning applications. One prominent application is the recognition of hand gestures used in communication with deaf individuals, specifically in identifying the dashed letters within Quranic surahs. This paper proposes a new alphabet-based Arabic sign language recognition model, which employs a vision-based approach. The system comprises four stages: data acquisition, data preprocessing, feature extraction, and classification. The proposed model accommodates three types of datasets: bare hands against a dark background, bare hands against a light background, and hands wearing dark-colored gloves. The process begins with capturing an image of the alphabet gesture, followed by hand separation and background isolation. Hand features are then extracted based on the chosen method. In terms of classification, supervised learning techniques are employed to classify the 28-letter Arabic alphabet using 9,240 images. The focus is on classifying the 14 alphabetic letters representing the initial Quranic surahs in the Quranic sign language. The experimental results demonstrate that the new proposed model has achieved an impressive accuracy of 99.5% using the k nearest neighbor classifier.

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Section: Image Preprocessing and Hand Detectionmentioning

confidence: 99%

Alphabet Recognition in Arabic Sign Language: A Machine Learning Perspective

Khattab,

Zeki,

Matter

et al. 2024

مجلة کلية الآداب بقنا

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“…Moreover, the SSIM value varies from zero to one so that the higher the SSIM value, the higher the similarity. Thus, reconstructed images are more effective [39,40].…”

Section: Performance Analysismentioning

confidence: 99%

A Hybrid Regularization-Based Multi-Frame Super-Resolution Using Bayesian Framework

Khattab¹,

Zeki²,

Alwan³

et al. 2023

Computer Systems Science and Engineering

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The prime purpose for the image reconstruction of a multi-frame superresolution is to reconstruct a higher-resolution image through incorporating the knowledge obtained from a series of relevant low-resolution images, which is useful in numerous fields. Nevertheless, super-resolution image reconstruction methods are usually damaged by undesirable restorative artifacts, which include blurring distortion, noises, and stair-casing effects. Consequently, it is always challenging to achieve balancing between image smoothness and preservation of the edges inside the image. In this research work, we seek to increase the effectiveness of multi-frame super-resolution image reconstruction by increasing the visual information and improving the automated machine perception, which improves human analysis and interpretation processes. Accordingly, we propose a new approach to the image reconstruction of multi-frame super-resolution, so that it is created through the use of the regularization framework. In the proposed approach, the bilateral edge preserving and bilateral total variation regularizations are employed to approximate a high-resolution image generated from a sequence of corresponding images with low-resolution to protect significant features of an image, including sharp image edges and texture details while preventing artifacts. The experimental results of the synthesized image demonstrate that the new proposed approach has improved efficacy both visually and numerically more than other approaches.

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“…Phase. When the images of ArSL have been extracted from the camera, they are sent on to the preprocessing phase, which is comprised of a number of primary processes [66,67]. Te images used as data are those of a depth that feature a hand.…”

Section: Collect Dataset and Image Preprocessingmentioning

confidence: 99%

Integrated Mediapipe with a CNN Model for Arabic Sign Language Recognition

Moustafa,

Mohd Rahim,

Bouallegue

et al. 2023

Journal of Electrical and Computer Engineering

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Deaf and dumb people struggle with communicating on a day-to-day basis. Current advancements in artificial intelligence (AI) have allowed this communication barrier to be removed. A letter recognition system for Arabic sign language (ArSL) has been developed as a result of this effort. The deep convolutional neural network (CNN) structure is used by the ArSL recognition system in order to process depth data and to improve the ability for hearing-impaired to communicate with others. In the proposed model, letters of the hand-sign alphabet and the Arabic alphabet would be recognized and identified automatically based on user input. The proposed model should be able to identify ArSL with a rate of accuracy of 97.1%. In order to test our approach, we carried out a comparative study and discovered that it is able to differentiate between static indications with a higher level of accuracy than prior studies had achieved using the same dataset.

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Regularized Multiframe Super-Resolution Image Reconstruction Using Linear and Nonlinear Filters

Cited by 7 publications

References 38 publications

Alphabet Recognition in Arabic Sign Language: A Machine Learning Perspective

Alphabet Recognition in Arabic Sign Language: A Machine Learning Perspective

A Hybrid Regularization-Based Multi-Frame Super-Resolution Using Bayesian Framework

Integrated Mediapipe with a CNN Model for Arabic Sign Language Recognition

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