Hybrid Convolutional Transformer with Dynamic Prompting for Adaptive Image Restoration Article Swipe
Jinmei Zhang
,
Guorong Chen
,
Junliang Yang
,
Qingru Zhang
,
Shaofeng Liu
,
Weijie Zhang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/math13203329
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/math13203329
High-quality image restoration (IR) is a fundamental task in computer vision, aiming to recover a clear image from its degraded version. Prevailing methods typically employ a static inference pipeline, neglecting the spatial variability of image content and degradation, which makes it difficult for them to adaptively handle complex and diverse restoration scenarios. To address this issue, we propose a novel adaptive image restoration framework named Hybrid Convolutional Transformer with Dynamic Prompting (HCTDP). Our approach introduces two key architectural innovations: a Spatially Aware Dynamic Prompt Head Attention (SADPHA) module, which performs fine-grained local restoration by generating spatially variant prompts through real-time analysis of image content and a Gated Skip-Connection (GSC) module that refines multi-scale feature flow using efficient channel attention. To guide the network in generating more visually plausible results, the framework is optimized with a hybrid objective function that combines a pixel-wise L1 loss and a feature-level perceptual loss. Extensive experiments on multiple public benchmarks, including image deraining, dehazing, and denoising, demonstrate that our proposed HCTDP exhibits superior performance in both quantitative and qualitative evaluations, validating the effectiveness of the adaptive restoration framework while utilizing fewer parameters than key competitors.
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- article
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- https://doi.org/10.3390/math13203329
- https://www.mdpi.com/2227-7390/13/20/3329/pdf?version=1760935052
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- 19
- OpenAlex ID
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https://doi.org/10.3390/math13203329Digital Object Identifier
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Hybrid Convolutional Transformer with Dynamic Prompting for Adaptive Image RestorationWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-10-19Full publication date if available
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Jinmei Zhang, Guorong Chen, Junliang Yang, Qingru Zhang, Shaofeng Liu, Weijie ZhangList of authors in order
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https://doi.org/10.3390/math13203329Publisher landing page
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https://www.mdpi.com/2227-7390/13/20/3329/pdf?version=1760935052Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2227-7390/13/20/3329/pdf?version=1760935052Direct OA link when available
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0Total citation count in OpenAlex
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| abstract_inverted_index.superior | 167 |
| abstract_inverted_index.version. | 20 |
| abstract_inverted_index.visually | 126 |
| abstract_inverted_index.Attention | 85 |
| abstract_inverted_index.Extensive | 149 |
| abstract_inverted_index.Prompting | 70 |
| abstract_inverted_index.Spatially | 80 |
| abstract_inverted_index.dehazing, | 158 |
| abstract_inverted_index.difficult | 41 |
| abstract_inverted_index.efficient | 116 |
| abstract_inverted_index.framework | 63, 130, 182 |
| abstract_inverted_index.including | 155 |
| abstract_inverted_index.inference | 27 |
| abstract_inverted_index.objective | 136 |
| abstract_inverted_index.optimized | 132 |
| abstract_inverted_index.pipeline, | 28 |
| abstract_inverted_index.plausible | 127 |
| abstract_inverted_index.real-time | 99 |
| abstract_inverted_index.spatially | 95 |
| abstract_inverted_index.typically | 23 |
| abstract_inverted_index.utilizing | 184 |
| abstract_inverted_index.Prevailing | 21 |
| abstract_inverted_index.adaptively | 45 |
| abstract_inverted_index.attention. | 118 |
| abstract_inverted_index.denoising, | 160 |
| abstract_inverted_index.deraining, | 157 |
| abstract_inverted_index.generating | 94, 124 |
| abstract_inverted_index.introduces | 74 |
| abstract_inverted_index.neglecting | 29 |
| abstract_inverted_index.parameters | 186 |
| abstract_inverted_index.perceptual | 147 |
| abstract_inverted_index.pixel-wise | 141 |
| abstract_inverted_index.scenarios. | 51 |
| abstract_inverted_index.validating | 175 |
| abstract_inverted_index.Transformer | 67 |
| abstract_inverted_index.benchmarks, | 154 |
| abstract_inverted_index.demonstrate | 161 |
| abstract_inverted_index.experiments | 150 |
| abstract_inverted_index.fundamental | 6 |
| abstract_inverted_index.multi-scale | 112 |
| abstract_inverted_index.performance | 168 |
| abstract_inverted_index.qualitative | 173 |
| abstract_inverted_index.restoration | 2, 50, 62, 92, 181 |
| abstract_inverted_index.variability | 32 |
| abstract_inverted_index.High-quality | 0 |
| abstract_inverted_index.competitors. | 189 |
| abstract_inverted_index.degradation, | 37 |
| abstract_inverted_index.evaluations, | 174 |
| abstract_inverted_index.fine-grained | 90 |
| abstract_inverted_index.innovations: | 78 |
| abstract_inverted_index.quantitative | 171 |
| abstract_inverted_index.Convolutional | 66 |
| abstract_inverted_index.architectural | 77 |
| abstract_inverted_index.effectiveness | 177 |
| abstract_inverted_index.feature-level | 146 |
| abstract_inverted_index.Skip-Connection | 107 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.52576395 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |