Multi-scale Self-attention Recursive Hierarchical Network Based on Improved Residual Conv-LSTM Article Swipe
Xiang Lv
,
Changzhong Wang
,
Yang Zhang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-3388990/v1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-3388990/v1
Super-resolution Reconstruction (SR) refers to the process of obtaining high resolution (HR) images from low resolution (LR) images. In recent years, with the vigorous development of deep learning, SR technology has been more widely applied, especially those based on convolution neural network (CNN). Although some existing methods use multi-scale convolution for feature extraction, most of them improve network performance by deepening network depth, often ignoring the intrinsic correlation between different hierarchic features. As is known to all, the increase of network depth may lead to training difficulties, insufficient feature extraction, feature details loss and other problems, which seriously limit the applications of network models in practice. Based on this, this paper proposes a multi-scale self-attention recursive hierarchical network (M-SRHN) based on improved residual Conv-LSTM. The proposed network model can achieve good reconstruction performance without sacrificing too many resources. Specifically, a multiscale guided learning self-attentional residual block (MGSRB) is first designed. It has four dilated convolutions and an enhanced spatial self-attention mechanism (ESA). This block can adaptively recalibrate the response of feature space by explicitly modeling the interdependence between spaces. Then, an improved residual LSTM (IRC-LSTM) network is proposed to memorize the output features of MGSRB, which can effectively process and store the memorized feature information. Further, a deep pyramid hierarchical module (DPHB) is used to extract more effective hierarchical information, and multiple MGSRBs, IRC-LSTM and DPHB are stacked to form the main framework of our network. Finally, a recursive subpixel reconstruction network is used to reconstruct images. Compared with some state-of-the-art SR methods, the proposed method had better reconstruction performance.
Related Topics
Concepts
Computer science
Residual
Block (permutation group theory)
Artificial intelligence
Feature (linguistics)
Convolution (computer science)
Pattern recognition (psychology)
Process (computing)
Pyramid (geometry)
Feature extraction
Backbone network
Artificial neural network
Machine learning
Algorithm
Mathematics
Computer network
Philosophy
Geometry
Linguistics
Operating system
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-3388990/v1
- https://www.researchsquare.com/article/rs-3388990/latest.pdf
- OA Status
- green
- References
- 92
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387344849
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387344849Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21203/rs.3.rs-3388990/v1Digital Object Identifier
- Title
-
Multi-scale Self-attention Recursive Hierarchical Network Based on Improved Residual Conv-LSTMWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-10-04Full publication date if available
- Authors
-
Xiang Lv, Changzhong Wang, Yang ZhangList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-3388990/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-3388990/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.researchsquare.com/article/rs-3388990/latest.pdfDirect OA link when available
- Concepts
-
Computer science, Residual, Block (permutation group theory), Artificial intelligence, Feature (linguistics), Convolution (computer science), Pattern recognition (psychology), Process (computing), Pyramid (geometry), Feature extraction, Backbone network, Artificial neural network, Machine learning, Algorithm, Mathematics, Computer network, Philosophy, Geometry, Linguistics, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
92Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W1811400895, https://openalex.org/W6675547922, https://openalex.org/W2890330644, https://openalex.org/W2146200771, https://openalex.org/W2134954424, https://openalex.org/W2029684123, https://openalex.org/W2137290314, https://openalex.org/W2111454493, https://openalex.org/W1995228944, https://openalex.org/W2039539938, https://openalex.org/W1992408872, https://openalex.org/W2123613719, https://openalex.org/W2032024719, https://openalex.org/W2093633095, https://openalex.org/W6676792524, https://openalex.org/W2080875060, https://openalex.org/W2157494358, https://openalex.org/W2122905512, https://openalex.org/W2167706775, https://openalex.org/W2157190232, https://openalex.org/W2124378283, https://openalex.org/W2047920195, https://openalex.org/W54257720, https://openalex.org/W2503339013, https://openalex.org/W1885185971, https://openalex.org/W2242218935, https://openalex.org/W2214802144, https://openalex.org/W2788047482, https://openalex.org/W2762292478, https://openalex.org/W2607041014, https://openalex.org/W2799120945, https://openalex.org/W2866634454, https://openalex.org/W2476548250, https://openalex.org/W2747898905, https://openalex.org/W6725739302, https://openalex.org/W2523714292, https://openalex.org/W2735224642, https://openalex.org/W6755102754, https://openalex.org/W6749094794, https://openalex.org/W6753412334, https://openalex.org/W2891158090, https://openalex.org/W3032506565, https://openalex.org/W2947376905, https://openalex.org/W2911641177, https://openalex.org/W3104028135, https://openalex.org/W3143401236, https://openalex.org/W2895240252, https://openalex.org/W3087455782, https://openalex.org/W3169328504, https://openalex.org/W1485009520, https://openalex.org/W2109255472, https://openalex.org/W2508741746, https://openalex.org/W6751733626, https://openalex.org/W2630837129, https://openalex.org/W1923697677, https://openalex.org/W2922435819, https://openalex.org/W1791560514, https://openalex.org/W2110158442, https://openalex.org/W1930824406, https://openalex.org/W2192954843, https://openalex.org/W935139217, https://openalex.org/W2790610275, https://openalex.org/W2743529218, https://openalex.org/W2896927224, https://openalex.org/W2935564801, https://openalex.org/W2939795759, https://openalex.org/W2946362479, https://openalex.org/W3035378062, https://openalex.org/W3071046106, https://openalex.org/W2133665775, https://openalex.org/W4377561911, https://openalex.org/W2884585870, https://openalex.org/W2963645458, https://openalex.org/W4320013936, https://openalex.org/W3175873985, https://openalex.org/W2997225633, https://openalex.org/W4299576237, https://openalex.org/W2963182372, https://openalex.org/W2963470893, https://openalex.org/W2109453625, https://openalex.org/W3034362507, https://openalex.org/W4249436546, https://openalex.org/W2963446712, https://openalex.org/W1967441049, https://openalex.org/W2965161890, https://openalex.org/W3179253672, https://openalex.org/W2964101377, https://openalex.org/W2963372104, https://openalex.org/W2895598217, https://openalex.org/W4288026253, https://openalex.org/W2964125708, https://openalex.org/W3170026688 |
| referenced_works_count | 92 |
| abstract_inverted_index.a | 113, 140, 207, 238 |
| abstract_inverted_index.As | 73 |
| abstract_inverted_index.In | 19 |
| abstract_inverted_index.It | 151 |
| abstract_inverted_index.SR | 29, 252 |
| abstract_inverted_index.an | 157, 181 |
| abstract_inverted_index.by | 60, 173 |
| abstract_inverted_index.in | 105 |
| abstract_inverted_index.is | 74, 148, 187, 213, 243 |
| abstract_inverted_index.of | 8, 26, 55, 80, 102, 170, 194, 234 |
| abstract_inverted_index.on | 39, 108, 121 |
| abstract_inverted_index.to | 5, 76, 85, 189, 215, 229, 245 |
| abstract_inverted_index.The | 125 |
| abstract_inverted_index.and | 94, 156, 200, 221, 225 |
| abstract_inverted_index.are | 227 |
| abstract_inverted_index.can | 129, 165, 197 |
| abstract_inverted_index.for | 51 |
| abstract_inverted_index.had | 257 |
| abstract_inverted_index.has | 31, 152 |
| abstract_inverted_index.low | 15 |
| abstract_inverted_index.may | 83 |
| abstract_inverted_index.our | 235 |
| abstract_inverted_index.the | 6, 23, 66, 78, 100, 168, 176, 191, 202, 231, 254 |
| abstract_inverted_index.too | 136 |
| abstract_inverted_index.use | 48 |
| abstract_inverted_index.(HR) | 12 |
| abstract_inverted_index.(LR) | 17 |
| abstract_inverted_index.(SR) | 3 |
| abstract_inverted_index.DPHB | 226 |
| abstract_inverted_index.LSTM | 184 |
| abstract_inverted_index.This | 163 |
| abstract_inverted_index.all, | 77 |
| abstract_inverted_index.been | 32 |
| abstract_inverted_index.deep | 27, 208 |
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| abstract_inverted_index.four | 153 |
| abstract_inverted_index.from | 14 |
| abstract_inverted_index.good | 131 |
| abstract_inverted_index.high | 10 |
| abstract_inverted_index.lead | 84 |
| abstract_inverted_index.loss | 93 |
| abstract_inverted_index.main | 232 |
| abstract_inverted_index.many | 137 |
| abstract_inverted_index.more | 33, 217 |
| abstract_inverted_index.most | 54 |
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| abstract_inverted_index.this | 110 |
| abstract_inverted_index.used | 214, 244 |
| abstract_inverted_index.with | 22, 249 |
| abstract_inverted_index.Based | 107 |
| abstract_inverted_index.Then, | 180 |
| abstract_inverted_index.based | 38, 120 |
| abstract_inverted_index.block | 146, 164 |
| abstract_inverted_index.depth | 82 |
| abstract_inverted_index.first | 149 |
| abstract_inverted_index.known | 75 |
| abstract_inverted_index.limit | 99 |
| abstract_inverted_index.model | 128 |
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| abstract_inverted_index.this, | 109 |
| abstract_inverted_index.those | 37 |
| abstract_inverted_index.which | 97, 196 |
| abstract_inverted_index.(CNN). | 43 |
| abstract_inverted_index.(DPHB) | 212 |
| abstract_inverted_index.(ESA). | 162 |
| abstract_inverted_index.MGSRB, | 195 |
| abstract_inverted_index.better | 258 |
| abstract_inverted_index.depth, | 63 |
| abstract_inverted_index.guided | 142 |
| abstract_inverted_index.images | 13 |
| abstract_inverted_index.method | 256 |
| abstract_inverted_index.models | 104 |
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| abstract_inverted_index.refers | 4 |
| abstract_inverted_index.widely | 34 |
| abstract_inverted_index.years, | 21 |
| abstract_inverted_index.(MGSRB) | 147 |
| abstract_inverted_index.MGSRBs, | 223 |
| abstract_inverted_index.achieve | 130 |
| abstract_inverted_index.between | 69, 178 |
| abstract_inverted_index.details | 92 |
| abstract_inverted_index.dilated | 154 |
| abstract_inverted_index.extract | 216 |
| abstract_inverted_index.feature | 52, 89, 91, 171, 204 |
| abstract_inverted_index.images. | 18, 247 |
| abstract_inverted_index.improve | 57 |
| abstract_inverted_index.methods | 47 |
| abstract_inverted_index.network | 42, 58, 62, 81, 103, 118, 127, 186, 242 |
| abstract_inverted_index.process | 7, 199 |
| abstract_inverted_index.pyramid | 209 |
| abstract_inverted_index.spaces. | 179 |
| abstract_inverted_index.spatial | 159 |
| abstract_inverted_index.stacked | 228 |
| abstract_inverted_index.without | 134 |
| abstract_inverted_index.(M-SRHN) | 119 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Although | 44 |
| abstract_inverted_index.Compared | 248 |
| abstract_inverted_index.Finally, | 237 |
| abstract_inverted_index.Further, | 206 |
| abstract_inverted_index.IRC-LSTM | 224 |
| abstract_inverted_index.applied, | 35 |
| abstract_inverted_index.enhanced | 158 |
| abstract_inverted_index.existing | 46 |
| abstract_inverted_index.features | 193 |
| abstract_inverted_index.ignoring | 65 |
| abstract_inverted_index.improved | 122, 182 |
| abstract_inverted_index.increase | 79 |
| abstract_inverted_index.learning | 143 |
| abstract_inverted_index.memorize | 190 |
| abstract_inverted_index.methods, | 253 |
| abstract_inverted_index.modeling | 175 |
| abstract_inverted_index.multiple | 222 |
| abstract_inverted_index.network. | 236 |
| abstract_inverted_index.proposed | 126, 188, 255 |
| abstract_inverted_index.proposes | 112 |
| abstract_inverted_index.residual | 123, 145, 183 |
| abstract_inverted_index.response | 169 |
| abstract_inverted_index.subpixel | 240 |
| abstract_inverted_index.training | 86 |
| abstract_inverted_index.vigorous | 24 |
| abstract_inverted_index.deepening | 61 |
| abstract_inverted_index.designed. | 150 |
| abstract_inverted_index.different | 70 |
| abstract_inverted_index.effective | 218 |
| abstract_inverted_index.features. | 72 |
| abstract_inverted_index.framework | 233 |
| abstract_inverted_index.intrinsic | 67 |
| abstract_inverted_index.learning, | 28 |
| abstract_inverted_index.mechanism | 161 |
| abstract_inverted_index.memorized | 203 |
| abstract_inverted_index.obtaining | 9 |
| abstract_inverted_index.practice. | 106 |
| abstract_inverted_index.problems, | 96 |
| abstract_inverted_index.recursive | 116, 239 |
| abstract_inverted_index.seriously | 98 |
| abstract_inverted_index.(IRC-LSTM) | 185 |
| abstract_inverted_index.Conv-LSTM. | 124 |
| abstract_inverted_index.adaptively | 166 |
| abstract_inverted_index.especially | 36 |
| abstract_inverted_index.explicitly | 174 |
| abstract_inverted_index.hierarchic | 71 |
| abstract_inverted_index.multiscale | 141 |
| abstract_inverted_index.resolution | 11, 16 |
| abstract_inverted_index.resources. | 138 |
| abstract_inverted_index.technology | 30 |
| abstract_inverted_index.convolution | 40, 50 |
| abstract_inverted_index.correlation | 68 |
| abstract_inverted_index.development | 25 |
| abstract_inverted_index.effectively | 198 |
| abstract_inverted_index.extraction, | 53, 90 |
| abstract_inverted_index.multi-scale | 49, 114 |
| abstract_inverted_index.performance | 59, 133 |
| abstract_inverted_index.recalibrate | 167 |
| abstract_inverted_index.reconstruct | 246 |
| abstract_inverted_index.sacrificing | 135 |
| abstract_inverted_index.applications | 101 |
| abstract_inverted_index.convolutions | 155 |
| abstract_inverted_index.hierarchical | 117, 210, 219 |
| abstract_inverted_index.information, | 220 |
| abstract_inverted_index.information. | 205 |
| abstract_inverted_index.insufficient | 88 |
| abstract_inverted_index.performance. | 260 |
| abstract_inverted_index.Specifically, | 139 |
| abstract_inverted_index.difficulties, | 87 |
| abstract_inverted_index.Reconstruction | 2 |
| abstract_inverted_index.reconstruction | 132, 241, 259 |
| abstract_inverted_index.self-attention | 115, 160 |
| abstract_inverted_index.interdependence | 177 |
| abstract_inverted_index.Super-resolution | 1 |
| abstract_inverted_index.self-attentional | 144 |
| abstract_inverted_index.state-of-the-art | 251 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.14583203 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |