Comprehensive Overview of Backpropagation Algorithm for Digital Image Denoising

Artificial ANNs (ANNs) are relatively new computational tools used in the development of intelligent systems, some of which are inspired by biological ANNs, and have found widespread application in the solving of a variety of complex real-world problems. It boasts enticing features as well as remark...

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Published in:	Electronics (Basel) Vol. 11; no. 10; p. 1590
Main Authors:	Singh, Abha, Kushwaha, Sumit, Alarfaj, Maryam, Singh, Manoj
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.05.2022
Subjects:	Algorithms Back propagation Blurring Data processing Digital imaging Neural networks Neurons Noise Noise reduction Probabilistic analysis Self-similarity Signal processing Software
ISSN:	2079-9292, 2079-9292
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Abstract	Artificial ANNs (ANNs) are relatively new computational tools used in the development of intelligent systems, some of which are inspired by biological ANNs, and have found widespread application in the solving of a variety of complex real-world problems. It boasts enticing features as well as remarkable data processing capabilities. In this paper, a comprehensive overview of the backpropagation algorithm for digital image denoising was discussed. Then, we presented a probabilistic analysis of how different algorithms address this challenge, arguing that small ANNs can denoise small-scale texture patterns almost as effectively as their larger equivalents. The results also show that self-similarity and ANNs are complementary paradigms for patch denoising, as demonstrated by an algorithm that effectively complements BM3D with small ANNs, surpassing BM3D at a low cost. Here, one of the most significant advantages of this learning technique is that, once taught, digital images may be recovered without prior knowledge of the degradation model (noise/blurring) that caused the digital image to become distorted.
AbstractList	Artificial ANNs (ANNs) are relatively new computational tools used in the development of intelligent systems, some of which are inspired by biological ANNs, and have found widespread application in the solving of a variety of complex real-world problems. It boasts enticing features as well as remarkable data processing capabilities. In this paper, a comprehensive overview of the backpropagation algorithm for digital image denoising was discussed. Then, we presented a probabilistic analysis of how different algorithms address this challenge, arguing that small ANNs can denoise small-scale texture patterns almost as effectively as their larger equivalents. The results also show that self-similarity and ANNs are complementary paradigms for patch denoising, as demonstrated by an algorithm that effectively complements BM3D with small ANNs, surpassing BM3D at a low cost. Here, one of the most significant advantages of this learning technique is that, once taught, digital images may be recovered without prior knowledge of the degradation model (noise/blurring) that caused the digital image to become distorted.
Author	Alarfaj, Maryam Singh, Abha Kushwaha, Sumit Singh, Manoj
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Cites_doi	10.13005/bpj/1240 10.1109/TIP.2010.2073477 10.1007/s10278-018-0110-y 10.3390/rs13142771 10.1016/j.neunet.2019.04.024 10.2174/1573405614666180801113345 10.1109/TCSVT.2019.2951778 10.1016/j.isatra.2019.11.008 10.1109/TIP.2022.3162961 10.1007/3-540-49430-8_2 10.1109/79.581363 10.1109/CVPR.2011.5995309 10.1049/iet-ipr.2019.0157 10.13005/bpj/1175 10.1016/S0167-7012(00)00201-3 10.21236/AD0256582 10.1515/bmt-2018-0101 10.1109/ACCESS.2019.2930319 10.13005/bpj/1171 10.1002/9780470512517 10.1117/1.JEI.28.1.013011 10.1109/LGRS.2020.2993899 10.1109/TIP.2006.881969 10.4066/biomedicalresearch.29-18-853 10.3390/app11209616 10.1007/s10044-019-00845-9
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SubjectTerms	Algorithms Back propagation Blurring Data processing Digital imaging Neural networks Neurons Noise Noise reduction Probabilistic analysis Self-similarity Signal processing Software
Title	Comprehensive Overview of Backpropagation Algorithm for Digital Image Denoising
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