ResDNN: deep residual learning for natural image denoising Article Swipe
Gurprem Singh
,
Ajay Mittal
,
Naveen Aggarwal
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1049/iet-ipr.2019.0623
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1049/iet-ipr.2019.0623
Image denoising is a thoroughly studied research problem in the areas of image processing and computer vision. In this work, a deep convolution neural network with added benefits of residual learning for image denoising is proposed. The network is composed of convolution layers and ResNet blocks along with rectified linear unit activation functions. The network is capable of learning end‐to‐end mappings from noise distorted images to restored cleaner versions. The deeper networks tend to be challenging to train and often are posed with the problem of vanishing gradients. The residual learning and orthogonal kernel initialisation keep the gradients in check. The skip connections in the ResNet blocks pass on the learned abstractions further down the network in the forward pass, thus achieving better results. With a single model, one can tackle different levels of Gaussian noise efficiently. The experiments conducted on the benchmark datasets prove that the proposed model obtains a significant improvement in structural similarity index than the previously existing state‐of‐the‐art techniques.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1049/iet-ipr.2019.0623
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/iet-ipr.2019.0623
- OA Status
- bronze
- Cited By
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- References
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- Related Works
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- OpenAlex ID
- https://openalex.org/W3017169454
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3017169454Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1049/iet-ipr.2019.0623Digital Object Identifier
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ResDNN: deep residual learning for natural image denoisingWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-04-17Full publication date if available
- Authors
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Gurprem Singh, Ajay Mittal, Naveen AggarwalList of authors in order
- Landing page
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https://doi.org/10.1049/iet-ipr.2019.0623Publisher landing page
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/iet-ipr.2019.0623Direct link to full text PDF
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bronzeOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/iet-ipr.2019.0623Direct OA link when available
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Residual, Artificial intelligence, Image denoising, Computer science, Noise reduction, Deep learning, Natural (archaeology), Pattern recognition (psychology), Image (mathematics), Computer vision, Algorithm, Geology, PaleontologyTop concepts (fields/topics) attached by OpenAlex
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29Total citation count in OpenAlex
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2025: 8, 2024: 5, 2023: 8, 2022: 5, 2021: 3Per-year citation counts (last 5 years)
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41Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.for | 31 |
| abstract_inverted_index.one | 128 |
| abstract_inverted_index.the | 9, 83, 96, 104, 109, 114, 117, 141, 146, 158 |
| abstract_inverted_index.With | 124 |
| abstract_inverted_index.deep | 21 |
| abstract_inverted_index.down | 113 |
| abstract_inverted_index.from | 61 |
| abstract_inverted_index.keep | 95 |
| abstract_inverted_index.pass | 107 |
| abstract_inverted_index.skip | 101 |
| abstract_inverted_index.tend | 72 |
| abstract_inverted_index.than | 157 |
| abstract_inverted_index.that | 145 |
| abstract_inverted_index.this | 18 |
| abstract_inverted_index.thus | 120 |
| abstract_inverted_index.unit | 50 |
| abstract_inverted_index.with | 25, 47, 82 |
| abstract_inverted_index.Image | 0 |
| abstract_inverted_index.added | 26 |
| abstract_inverted_index.along | 46 |
| abstract_inverted_index.areas | 10 |
| abstract_inverted_index.image | 12, 32 |
| abstract_inverted_index.index | 156 |
| abstract_inverted_index.model | 148 |
| abstract_inverted_index.noise | 62, 135 |
| abstract_inverted_index.often | 79 |
| abstract_inverted_index.pass, | 119 |
| abstract_inverted_index.posed | 81 |
| abstract_inverted_index.prove | 144 |
| abstract_inverted_index.train | 77 |
| abstract_inverted_index.work, | 19 |
| abstract_inverted_index.ResNet | 44, 105 |
| abstract_inverted_index.better | 122 |
| abstract_inverted_index.blocks | 45, 106 |
| abstract_inverted_index.check. | 99 |
| abstract_inverted_index.deeper | 70 |
| abstract_inverted_index.images | 64 |
| abstract_inverted_index.kernel | 93 |
| abstract_inverted_index.layers | 42 |
| abstract_inverted_index.levels | 132 |
| abstract_inverted_index.linear | 49 |
| abstract_inverted_index.model, | 127 |
| abstract_inverted_index.neural | 23 |
| abstract_inverted_index.single | 126 |
| abstract_inverted_index.tackle | 130 |
| abstract_inverted_index.capable | 56 |
| abstract_inverted_index.cleaner | 67 |
| abstract_inverted_index.forward | 118 |
| abstract_inverted_index.further | 112 |
| abstract_inverted_index.learned | 110 |
| abstract_inverted_index.network | 24, 37, 54, 115 |
| abstract_inverted_index.obtains | 149 |
| abstract_inverted_index.problem | 7, 84 |
| abstract_inverted_index.studied | 5 |
| abstract_inverted_index.vision. | 16 |
| abstract_inverted_index.Gaussian | 134 |
| abstract_inverted_index.benefits | 27 |
| abstract_inverted_index.composed | 39 |
| abstract_inverted_index.computer | 15 |
| abstract_inverted_index.datasets | 143 |
| abstract_inverted_index.existing | 160 |
| abstract_inverted_index.learning | 30, 58, 90 |
| abstract_inverted_index.mappings | 60 |
| abstract_inverted_index.networks | 71 |
| abstract_inverted_index.proposed | 147 |
| abstract_inverted_index.research | 6 |
| abstract_inverted_index.residual | 29, 89 |
| abstract_inverted_index.restored | 66 |
| abstract_inverted_index.results. | 123 |
| abstract_inverted_index.achieving | 121 |
| abstract_inverted_index.benchmark | 142 |
| abstract_inverted_index.conducted | 139 |
| abstract_inverted_index.denoising | 1, 33 |
| abstract_inverted_index.different | 131 |
| abstract_inverted_index.distorted | 63 |
| abstract_inverted_index.gradients | 97 |
| abstract_inverted_index.proposed. | 35 |
| abstract_inverted_index.rectified | 48 |
| abstract_inverted_index.vanishing | 86 |
| abstract_inverted_index.versions. | 68 |
| abstract_inverted_index.activation | 51 |
| abstract_inverted_index.functions. | 52 |
| abstract_inverted_index.gradients. | 87 |
| abstract_inverted_index.orthogonal | 92 |
| abstract_inverted_index.previously | 159 |
| abstract_inverted_index.processing | 13 |
| abstract_inverted_index.similarity | 155 |
| abstract_inverted_index.structural | 154 |
| abstract_inverted_index.thoroughly | 4 |
| abstract_inverted_index.challenging | 75 |
| abstract_inverted_index.connections | 102 |
| abstract_inverted_index.convolution | 22, 41 |
| abstract_inverted_index.experiments | 138 |
| abstract_inverted_index.improvement | 152 |
| abstract_inverted_index.significant | 151 |
| abstract_inverted_index.techniques. | 162 |
| abstract_inverted_index.abstractions | 111 |
| abstract_inverted_index.efficiently. | 136 |
| abstract_inverted_index.end‐to‐end | 59 |
| abstract_inverted_index.initialisation | 94 |
| abstract_inverted_index.state‐of‐the‐art | 161 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 95 |
| corresponding_author_ids | https://openalex.org/A5061479785 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I51452335 |
| citation_normalized_percentile.value | 0.85826961 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |