Ocean Remote Sensing Image Quality Assessment via Multidirectional Perception Fusion and Deviation-Aware Processing
Despite the visibility improvements achieved by existing methods, ocean remote sensing images often suffer from over-enhancement and texture distortion due to light attenuation and scattering, particularly when analyzing maritime objects like coral reefs, marine life, or submerged structures. Tradit...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10999047/ |
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| author | Chunjiang Liu Jingchun Zhou Bing Long Tianyuan Li Dehuan Zhang Weishi Zhang Gemine Vivone |
| author_facet | Chunjiang Liu Jingchun Zhou Bing Long Tianyuan Li Dehuan Zhang Weishi Zhang Gemine Vivone |
| author_sort | Chunjiang Liu |
| collection | DOAJ |
| description | Despite the visibility improvements achieved by existing methods, ocean remote sensing images often suffer from over-enhancement and texture distortion due to light attenuation and scattering, particularly when analyzing maritime objects like coral reefs, marine life, or submerged structures. Traditional assessment methods struggle to handle both over- and under-enhancement. To address this issue, we propose a novel ocean remote sensing quality assessment method that accurately captures diverse image quality deviations and aligns with human visual perception. To deeply analyze the relationship between the overall structure and details in ocean remote sensing enhancement, we introduce multidirectional perception fusion to enhance the perception of image details. To address the over-enhancement or under-enhancement regions, a diff capture block is designed to accurately detect and handle these deviations. In addition, a parallel processing architecture with a symmetric transform block performs a multidimensional analysis of score and weight features, balancing deviation regions against true reference regions. This process yields a comprehensive quality score derived from weighted calculations of activation scores and weights for each image block. Extensive experiments on ocean datasets featuring ocean objects and environments demonstrate that our method outperforms current quality assessment methods. In addition, we have conducted cross-dataset testing on remote sensing datasets that include complex terrains and natural landscapes, providing a more reliable assessment for geoscience applications such as ocean habitat mapping, underwater archaeology, and oceanographic research. |
| format | Article |
| id | doaj-art-65403ae59b0f416598c00cc0ac0b00fc |
| institution | DOAJ |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-65403ae59b0f416598c00cc0ac0b00fc2025-08-20T03:10:07ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118140001401410.1109/JSTARS.2025.356774910999047Ocean Remote Sensing Image Quality Assessment via Multidirectional Perception Fusion and Deviation-Aware ProcessingChunjiang Liu0https://orcid.org/0009-0003-1407-6866Jingchun Zhou1https://orcid.org/0000-0002-4111-6240Bing Long2Tianyuan Li3https://orcid.org/0009-0002-3773-8167Dehuan Zhang4Weishi Zhang5https://orcid.org/0000-0003-0519-8397Gemine Vivone6https://orcid.org/0000-0001-9542-0638College of Information Science and Technology, Dalian Maritime University, Dalian, ChinaCollege of Information Science and Technology, Dalian Maritime University, Dalian, ChinaIntelligent Policing Key Laboratory of Sichuan Province, Sichuan Police College, Luzhou, Sichuan, ChinaCollege of Information Science and Technology, Dalian Maritime University, Dalian, ChinaCollege of Information Science and Technology, Dalian Maritime University, Dalian, ChinaCollege of Information Science and Technology, Dalian Maritime University, Dalian, ChinaInstitute of Methodologies for Environmental Analysis, National Research Council, Tito, ItalyDespite the visibility improvements achieved by existing methods, ocean remote sensing images often suffer from over-enhancement and texture distortion due to light attenuation and scattering, particularly when analyzing maritime objects like coral reefs, marine life, or submerged structures. Traditional assessment methods struggle to handle both over- and under-enhancement. To address this issue, we propose a novel ocean remote sensing quality assessment method that accurately captures diverse image quality deviations and aligns with human visual perception. To deeply analyze the relationship between the overall structure and details in ocean remote sensing enhancement, we introduce multidirectional perception fusion to enhance the perception of image details. To address the over-enhancement or under-enhancement regions, a diff capture block is designed to accurately detect and handle these deviations. In addition, a parallel processing architecture with a symmetric transform block performs a multidimensional analysis of score and weight features, balancing deviation regions against true reference regions. This process yields a comprehensive quality score derived from weighted calculations of activation scores and weights for each image block. Extensive experiments on ocean datasets featuring ocean objects and environments demonstrate that our method outperforms current quality assessment methods. In addition, we have conducted cross-dataset testing on remote sensing datasets that include complex terrains and natural landscapes, providing a more reliable assessment for geoscience applications such as ocean habitat mapping, underwater archaeology, and oceanographic research.https://ieeexplore.ieee.org/document/10999047/Image assessmentimage enhancementno-reference (NR) image assessmentocean remote sensing image |
| spellingShingle | Chunjiang Liu Jingchun Zhou Bing Long Tianyuan Li Dehuan Zhang Weishi Zhang Gemine Vivone Ocean Remote Sensing Image Quality Assessment via Multidirectional Perception Fusion and Deviation-Aware Processing IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Image assessment image enhancement no-reference (NR) image assessment ocean remote sensing image |
| title | Ocean Remote Sensing Image Quality Assessment via Multidirectional Perception Fusion and Deviation-Aware Processing |
| title_full | Ocean Remote Sensing Image Quality Assessment via Multidirectional Perception Fusion and Deviation-Aware Processing |
| title_fullStr | Ocean Remote Sensing Image Quality Assessment via Multidirectional Perception Fusion and Deviation-Aware Processing |
| title_full_unstemmed | Ocean Remote Sensing Image Quality Assessment via Multidirectional Perception Fusion and Deviation-Aware Processing |
| title_short | Ocean Remote Sensing Image Quality Assessment via Multidirectional Perception Fusion and Deviation-Aware Processing |
| title_sort | ocean remote sensing image quality assessment via multidirectional perception fusion and deviation aware processing |
| topic | Image assessment image enhancement no-reference (NR) image assessment ocean remote sensing image |
| url | https://ieeexplore.ieee.org/document/10999047/ |
| work_keys_str_mv | AT chunjiangliu oceanremotesensingimagequalityassessmentviamultidirectionalperceptionfusionanddeviationawareprocessing AT jingchunzhou oceanremotesensingimagequalityassessmentviamultidirectionalperceptionfusionanddeviationawareprocessing AT binglong oceanremotesensingimagequalityassessmentviamultidirectionalperceptionfusionanddeviationawareprocessing AT tianyuanli oceanremotesensingimagequalityassessmentviamultidirectionalperceptionfusionanddeviationawareprocessing AT dehuanzhang oceanremotesensingimagequalityassessmentviamultidirectionalperceptionfusionanddeviationawareprocessing AT weishizhang oceanremotesensingimagequalityassessmentviamultidirectionalperceptionfusionanddeviationawareprocessing AT geminevivone oceanremotesensingimagequalityassessmentviamultidirectionalperceptionfusionanddeviationawareprocessing |