Spatially-Aware Loss Functions for GAN-Driven Super-Resolution Article Swipe
Xijun Wang
,
Santiago López-Tapia
,
Xinyi Wu
,
Rafael Molina
,
Aggelos K. Katsaggelos
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3579004
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3579004
Generative Adversarial Networks (GANs) have shown great performance on super-resolution problems since they can generate more visually realistic images and video frames. However, these models often introduce side effects into the outputs, such as unexpected artifacts and noises. To reduce these artifacts and enhance the perceptual quality of the results, in this paper, we propose a general method that can be effectively used in most GAN-based super-resolution (SR) models by introducing essential spatial information into the training process. We extract spatial information from the input data and incorporate it into the training loss, making the corresponding loss a spatially adaptive (SA) one. After that, we utilize it to guide the training process. We will show that the proposed approach is independent of the methods used to extract the spatial information and independent of the SR tasks and models. This method consistently guides the training process towards generating visually pleasing SR images and video frames, substantially mitigating artifacts and noise, ultimately leading to enhanced perceptual quality.
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- https://doi.org/10.1109/access.2025.3579004
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- 42
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https://openalex.org/W4411232113Canonical identifier for this work in OpenAlex
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https://doi.org/10.1109/access.2025.3579004Digital Object Identifier
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Spatially-Aware Loss Functions for GAN-Driven Super-ResolutionWork 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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2025-01-01Full publication date if available
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Xijun Wang, Santiago López-Tapia, Xinyi Wu, Rafael Molina, Aggelos K. KatsaggelosList of authors in order
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https://doi.org/10.1109/access.2025.3579004Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1109/access.2025.3579004Direct OA link when available
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Computer science, Resolution (logic), Image resolution, Optoelectronics, Materials science, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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42Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.images | 18, 150 |
| abstract_inverted_index.making | 93 |
| abstract_inverted_index.method | 57, 139 |
| abstract_inverted_index.models | 24, 68 |
| abstract_inverted_index.noise, | 158 |
| abstract_inverted_index.paper, | 52 |
| abstract_inverted_index.reduce | 39 |
| abstract_inverted_index.effects | 28 |
| abstract_inverted_index.enhance | 43 |
| abstract_inverted_index.extract | 79, 126 |
| abstract_inverted_index.frames, | 153 |
| abstract_inverted_index.frames. | 21 |
| abstract_inverted_index.general | 56 |
| abstract_inverted_index.leading | 160 |
| abstract_inverted_index.methods | 123 |
| abstract_inverted_index.models. | 137 |
| abstract_inverted_index.noises. | 37 |
| abstract_inverted_index.process | 144 |
| abstract_inverted_index.propose | 54 |
| abstract_inverted_index.quality | 46 |
| abstract_inverted_index.spatial | 72, 80, 128 |
| abstract_inverted_index.towards | 145 |
| abstract_inverted_index.utilize | 105 |
| abstract_inverted_index.However, | 22 |
| abstract_inverted_index.Networks | 2 |
| abstract_inverted_index.adaptive | 99 |
| abstract_inverted_index.approach | 118 |
| abstract_inverted_index.enhanced | 162 |
| abstract_inverted_index.generate | 14 |
| abstract_inverted_index.outputs, | 31 |
| abstract_inverted_index.pleasing | 148 |
| abstract_inverted_index.problems | 10 |
| abstract_inverted_index.process. | 77, 111 |
| abstract_inverted_index.proposed | 117 |
| abstract_inverted_index.quality. | 164 |
| abstract_inverted_index.results, | 49 |
| abstract_inverted_index.training | 76, 91, 110, 143 |
| abstract_inverted_index.visually | 16, 147 |
| abstract_inverted_index.GAN-based | 65 |
| abstract_inverted_index.artifacts | 35, 41, 156 |
| abstract_inverted_index.essential | 71 |
| abstract_inverted_index.introduce | 26 |
| abstract_inverted_index.realistic | 17 |
| abstract_inverted_index.spatially | 98 |
| abstract_inverted_index.Generative | 0 |
| abstract_inverted_index.generating | 146 |
| abstract_inverted_index.mitigating | 155 |
| abstract_inverted_index.perceptual | 45, 163 |
| abstract_inverted_index.ultimately | 159 |
| abstract_inverted_index.unexpected | 34 |
| abstract_inverted_index.Adversarial | 1 |
| abstract_inverted_index.effectively | 61 |
| abstract_inverted_index.incorporate | 87 |
| abstract_inverted_index.independent | 120, 131 |
| abstract_inverted_index.information | 73, 81, 129 |
| abstract_inverted_index.introducing | 70 |
| abstract_inverted_index.performance | 7 |
| abstract_inverted_index.consistently | 140 |
| abstract_inverted_index.corresponding | 95 |
| abstract_inverted_index.substantially | 154 |
| abstract_inverted_index.super-resolution | 9, 66 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.2168644 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |