Advances in Light Field Spatial Super-Resolution: A Comprehensive Literature Survey
Super-resolution reconstruction of light field images has recently become a central focus in the fields of computational photography and computer vision. We present a systematic review of 17 mainstream light field spatial super-resolution techniques, evaluating their performance across seven public...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10850612/ |
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| author | Wenqi Lyu Hao Sheng Wei Ke Xiao Ma |
| author_facet | Wenqi Lyu Hao Sheng Wei Ke Xiao Ma |
| author_sort | Wenqi Lyu |
| collection | DOAJ |
| description | Super-resolution reconstruction of light field images has recently become a central focus in the fields of computational photography and computer vision. We present a systematic review of 17 mainstream light field spatial super-resolution techniques, evaluating their performance across seven public datasets. Integrating experimental results, we specifically analyze the performance of deep learning-based super-resolution algorithms at various magnification levels. Although these models have made significant progress at lower magnifications (e.g., <inline-formula> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$4\times $ </tex-math></inline-formula>), current methods exhibit clear limitations at higher magnifications (e.g., <inline-formula> <tex-math notation="LaTeX">$8\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$16\times $ </tex-math></inline-formula>), particularly in maintaining structural integrity and disparity consistency. Our experimental findings indicate substantial differences in robustness and adaptability among methods: approaches such as DistgSSR and DPT perform exceptionally well at high magnifications, while others, like HLFSR, exhibit comparatively poorer performance in complex scenes. Additionally, the unique characteristics of light field images add complexity to the super-resolution task. Future research should focus on enhancing the robustness, generalization, and capability of algorithms to handle complex scenarios. This review offers valuable direction for future research on light field image super-resolution and provides a solid foundation for its applications in virtual reality, augmented reality, and autonomous driving. |
| format | Article |
| id | doaj-art-c4f0e86caf6148a3abc86630f8c453b7 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-c4f0e86caf6148a3abc86630f8c453b72025-01-31T00:01:52ZengIEEEIEEE Access2169-35362025-01-0113184701849710.1109/ACCESS.2025.353261010850612Advances in Light Field Spatial Super-Resolution: A Comprehensive Literature SurveyWenqi Lyu0https://orcid.org/0009-0002-9985-9044Hao Sheng1https://orcid.org/0000-0002-2811-8962Wei Ke2https://orcid.org/0000-0003-0952-0961Xiao Ma3Faculty of Applied Sciences, Macao Polytechnic University, Macau, SAR, ChinaState Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, ChinaFaculty of Applied Sciences, Macao Polytechnic University, Macau, SAR, ChinaFaculty of Applied Sciences, Macao Polytechnic University, Macau, SAR, ChinaSuper-resolution reconstruction of light field images has recently become a central focus in the fields of computational photography and computer vision. We present a systematic review of 17 mainstream light field spatial super-resolution techniques, evaluating their performance across seven public datasets. Integrating experimental results, we specifically analyze the performance of deep learning-based super-resolution algorithms at various magnification levels. Although these models have made significant progress at lower magnifications (e.g., <inline-formula> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$4\times $ </tex-math></inline-formula>), current methods exhibit clear limitations at higher magnifications (e.g., <inline-formula> <tex-math notation="LaTeX">$8\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$16\times $ </tex-math></inline-formula>), particularly in maintaining structural integrity and disparity consistency. Our experimental findings indicate substantial differences in robustness and adaptability among methods: approaches such as DistgSSR and DPT perform exceptionally well at high magnifications, while others, like HLFSR, exhibit comparatively poorer performance in complex scenes. Additionally, the unique characteristics of light field images add complexity to the super-resolution task. Future research should focus on enhancing the robustness, generalization, and capability of algorithms to handle complex scenarios. This review offers valuable direction for future research on light field image super-resolution and provides a solid foundation for its applications in virtual reality, augmented reality, and autonomous driving.https://ieeexplore.ieee.org/document/10850612/Light field imagespatial super-resolution reconstructionepipolar-plane imagedeep learning |
| spellingShingle | Wenqi Lyu Hao Sheng Wei Ke Xiao Ma Advances in Light Field Spatial Super-Resolution: A Comprehensive Literature Survey IEEE Access Light field image spatial super-resolution reconstruction epipolar-plane image deep learning |
| title | Advances in Light Field Spatial Super-Resolution: A Comprehensive Literature Survey |
| title_full | Advances in Light Field Spatial Super-Resolution: A Comprehensive Literature Survey |
| title_fullStr | Advances in Light Field Spatial Super-Resolution: A Comprehensive Literature Survey |
| title_full_unstemmed | Advances in Light Field Spatial Super-Resolution: A Comprehensive Literature Survey |
| title_short | Advances in Light Field Spatial Super-Resolution: A Comprehensive Literature Survey |
| title_sort | advances in light field spatial super resolution a comprehensive literature survey |
| topic | Light field image spatial super-resolution reconstruction epipolar-plane image deep learning |
| url | https://ieeexplore.ieee.org/document/10850612/ |
| work_keys_str_mv | AT wenqilyu advancesinlightfieldspatialsuperresolutionacomprehensiveliteraturesurvey AT haosheng advancesinlightfieldspatialsuperresolutionacomprehensiveliteraturesurvey AT weike advancesinlightfieldspatialsuperresolutionacomprehensiveliteraturesurvey AT xiaoma advancesinlightfieldspatialsuperresolutionacomprehensiveliteraturesurvey |