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Shear Let Transform Residual Learning Approach for Single-Image Super-Resolution

Computers, Materials & Continua 2024年第5期79卷 3193-3209页

作者： Israa Ismail Ghada Eltaweel mohamed meselhy eltouk.y Department of Computer Science Faculty of Computers and InformaticsSuez Canal UniversityIsmailia51422Egypt Department of Information Technology College of Computing and Information Technology at KhulaisUniversity of JeddahJeddahSaudi Arabia

Super-resolution techniques are employed to enhance image resolution by reconstructing high-resolution images from one or more low-resolution ***-resolution is of paramount importance in the context of remote sensing,satellite,aerial,security and surveillance ***-resolution remote sensing imagery is essential for surveillance and security purposes,enabling authorities to monitor remote or sensitive areas with greater *** study introduces a single-image super-resolution approach for remote sensing images,utilizing deep shearlet residual learning in the shearlet transform domain,and incorporating the Enhanced Deep Super-Resolution network(EDSR).Unlike conventional approaches that estimate residuals between high and low-resolution images,the proposed approach calculates the shearlet coefficients for the desired high-resolution image using the provided low-resolution image instead of estimating a residual image between the high-and low-resolution *** shearlet transform is chosen for its excellent sparse approximation ***,remote sensing images are transformed into the shearlet domain,which divides the input image into low and high *** shearlet coefficients are fed into the EDSR *** high-resolution image is subsequently reconstructed using the inverse shearlet *** incorporation of the EDSR network enhances training stability,leading to improved generated *** experimental results from the Deep Shearlet Residual Learning approach demonstrate its superior performance in remote sensing image recovery,effectively restoring both global topology and local edge detail information,thereby enhancing image *** to other networks,our proposed approach outperforms the state-of-the-art in terms of image quality,achieving an average peak signal-to-noise ratio of 35 and a structural similarity index measure of approximately 0.9.

关键词： Super-resolution shearlet transform shearlet coefficients enhanced deep super-resolution network

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Super-Resolution Based on Curvelet Transform and Sparse Representation

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Computer Systems Science & Engineering 2023年第4期45卷 167-181页

作者： Israa Ismail mohamed meselhy eltouk.y Ghada Eltaweel Department of Computer Science Faculty of Computers and InformaticsSuez Canal UniversityIsmailia51422Egypt Department of Information Technology University of JeddahCollege of Computing and Information Technology at KhulaisJeddahSaudi Arabia

Super-resolution techniques are used to reconstruct an image with a high resolution from one or more low-resolution image(s).In this paper,we proposed a single image super-resolution *** uses the nonlocal mean filter as a prior step to produce a denoised *** proposed algorithm is based on curvelet *** converts the denoised image into low and high frequencies(sub-bands).Then we applied a multi-dimensional interpolation called Lancozos interpolation over both *** parallel,we applied sparse representation with over complete dictionary for the denoised *** proposed algorithm then combines the dictionary learning in the sparse representation and the interpolated sub-bands using inverse curvelet transform to have an image with a higher *** experimental results of the proposed super-resolution algorithm show superior performance and obviously better-recovering images with enhanced *** comparison study shows that the proposed super-resolution algorithm outperforms the *** mean absolute error is 0.021±0.008 and the structural similarity index measure is 0.89±0.08.

关键词： Super-resolution Curvelet transform non-local means filter lancozos interpolation sparse representation

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Grey Wolf-Based Method for an Implicit Authentication of Smartphone Users

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Computers, Materials & Continua 2023年第5期75卷 3729-3741页

作者： Abdulwahab Ali Almazroi mohamed meselhy eltouk.y University of Jeddah College of Computing and Information Technology at KhulaisDepartment of Information TechnologyJeddahSaudi Arabia

Smartphones have now become an integral part of our everyday *** authentication on smartphones is often accomplished by mechanisms(like face unlock,pattern,or pin password)that authenticate the user’s *** technologies are simple,inexpensive,and fast for repeated ***,these technologies are still subject to assaults like smudge assaults and shoulder ***’touch behavior while using their cell phones might be used to authenticate them,which would solve the *** performance of the authentication process may be influenced by the attributes chosen(from these behaviors).The purpose of this study is to present an effective authentication technique that implicitly offers a better authentication method for smartphone usage while avoiding the cost of a particular device and considering the constrained capabilities of *** began by concentrating on feature selection methods utilizing the grey wolf optimization *** random forest classifier is used to evaluate these *** testing findings demonstrated that the grey wolf-based methodology works as a better optimum feature selection for building an implicit authentication mechanism for the smartphone environment when using a public *** achieved a 97.89%accuracy rate while utilizing just 16 of the 53 characteristics like utilizing minimum mobile resources mainly;processing power of the device and memory to validate ***,the findings revealed that our approach has a lower equal error rate(EER)of 0.5104,a false acceptance rate(FAR)of 1.00,and a false rejection rate(FRR)of 0.0209 compared to the methods discussed in the *** promising results will be used to create a mobile application that enables implicit validation of authorized users yet avoids current identification concerns and requires fewer mobile resources.

关键词： Smartphone authentication implicit authentication grey wolf random forest feature selection

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