Enhanced Learning Enriched Features Mechanism Using Deep Convolutional Neural Network for Image Denoising and Super-Resolution
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| Publicado en: | Applied Sciences vol. 14, no. 14 (2024), p. 6281 |
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| Autor principal: | |
| Otros Autores: | , , , , , , |
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MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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MARC
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| 001 | 3084778754 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2076-3417 | ||
| 024 | 7 | |a 10.3390/app14146281 |2 doi | |
| 035 | |a 3084778754 | ||
| 045 | 2 | |b d20240101 |b d20241231 | |
| 084 | |a 231338 |2 nlm | ||
| 100 | 1 | |a Iqra Waseem |u University Institute of Information Technology, PMAS Arid Agriculture University Rawalpindi, Rawalpindi 46000, Pakistan | |
| 245 | 1 | |a Enhanced Learning Enriched Features Mechanism Using Deep Convolutional Neural Network for Image Denoising and Super-Resolution | |
| 260 | |b MDPI AG |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Image denoising and super-resolution play vital roles in imaging systems, greatly reducing the preprocessing cost of many AI techniques for object detection, segmentation, and tracking. Various advancements have been accomplished in this field, but progress is still needed. In this paper, we have proposed a novel technique named the Enhanced Learning Enriched Features (ELEF) mechanism using a deep convolutional neural network, which makes significant improvements to existing techniques. ELEF consists of two major processes: (1) Denoising, which removes the noise from images; and (2) Super-resolution, which improves the clarity and details of images. Features are learned through deep CNN and not through traditional algorithms so that we can better refine and enhance images. To effectively capture features, the network architecture adopted Dual Attention Units (DUs), which align with the Multi-Scale Residual Block (MSRB) for robust feature extraction, working sidewise with the feature-matching Selective Kernel Extraction (SKF). In addition, resolution mismatching cases are processed in detail to produce high-quality images. The effectiveness of the ELEF model is highlighted by the performance metrics, achieving a Peak Signal-to-Noise Ratio (PSNR) of 42.99 and a Structural Similarity Index (SSIM) of 0.9889, which indicates the ability to carry out the desired high-quality image restoration and enhancement. | |
| 653 | |a Design | ||
| 653 | |a Dictionaries | ||
| 653 | |a Deep learning | ||
| 653 | |a Wavelet transforms | ||
| 653 | |a Algorithms | ||
| 653 | |a Signal processing | ||
| 653 | |a Neural networks | ||
| 700 | 1 | |a Habib, Muhammad |u University Institute of Information Technology, PMAS Arid Agriculture University Rawalpindi, Rawalpindi 46000, Pakistan | |
| 700 | 1 | |a Rehman, Eid |u Department of Computer Science & Information Technology, University of Mianwali, Mianwali 42200, Pakistan | |
| 700 | 1 | |a Ruqia Bibi |u University Institute of Information Technology, PMAS Arid Agriculture University Rawalpindi, Rawalpindi 46000, Pakistan | |
| 700 | 1 | |a Rehan Mehmood Yousaf |u University Institute of Information Technology, PMAS Arid Agriculture University Rawalpindi, Rawalpindi 46000, Pakistan | |
| 700 | 1 | |a Aslam, Muhammad |u Department of Computer Science, Aberystwyth University, Penglais, Aberystwyth SY23 3DB, UK | |
| 700 | 1 | |a Syeda Fizzah Jilani |u Department of Physics, Physical Sciences Building, Aberystwyth University, Aberystwyth SY23 3BZ, UK | |
| 700 | 1 | |a Muhammad Waqar Younis |u Department of Computer Science, Aberystwyth University, Penglais, Aberystwyth SY23 3DB, UK | |
| 773 | 0 | |t Applied Sciences |g vol. 14, no. 14 (2024), p. 6281 | |
| 786 | 0 | |d ProQuest |t Publicly Available Content Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3084778754/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3084778754/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3084778754/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |