PGHDR: Dynamic HDR reconstruction with progressive feature alignment and quality-guided fusion
Abstract High Dynamic Range (HDR) imaging captures the complete luminance information of a scene by fusing a multi-exposure image sequence. However, a key challenge remains: ghosting artifacts caused by object motion in dynamic scenes. Existing methods typically adopt an align-then-fuse strategy, of...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | https://doi.org/10.1007/s44443-025-00230-z |
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| _version_ | 1849225881321472000 |
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| author | Ying Qi Qiushi Li Zhaoyuan Huang Jian Li Chenyang Wang Teng Wan Qiang Zhang |
| author_facet | Ying Qi Qiushi Li Zhaoyuan Huang Jian Li Chenyang Wang Teng Wan Qiang Zhang |
| author_sort | Ying Qi |
| collection | DOAJ |
| description | Abstract High Dynamic Range (HDR) imaging captures the complete luminance information of a scene by fusing a multi-exposure image sequence. However, a key challenge remains: ghosting artifacts caused by object motion in dynamic scenes. Existing methods typically adopt an align-then-fuse strategy, often overlooking the spatial variability of alignment quality, which makes it difficult to balance ghosting suppression and detail preservation when handling complex motion. To address this issue, this paper proposes PGHDR, which leverages a quality-aware progressive alignment strategy. The core innovation is a learning paradigm that jointly optimizes motion estimation and alignment quality perception. This approach treats alignment quality as a learnable, spatially-varying confidence and materializes this concept through three synergistic modules. The Progressive Deformable Feature Alignment (PDFA) module achieves robust feature extraction through a two-stage deformable convolution and exposure-aware modulation. The Quality-Guided Motion Compensation (QGMC) module jointly learns optical flow and a confidence mask, enabling reliability-aware motion estimation. The Structure-Preserving Adaptive Fusion (SPAF) module performs weighted fusion based on the learned quality information while preserving image details via a local structure constraint. Experiments show that PGHDR achieves a PSNR- $$\ell $$ ℓ of 42.58 dB and an HDR-VDP-2 score of 67.15. Visual evaluations on multiple datasets demonstrate that our method significantly outperforms existing approaches, achieving high-quality, ghost-free HDR reconstruction. |
| format | Article |
| id | doaj-art-f08df7a357b146e2bea2beed57faf88f |
| institution | Kabale University |
| issn | 1319-1578 2213-1248 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-f08df7a357b146e2bea2beed57faf88f2025-08-24T11:53:37ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782213-12482025-08-0137712310.1007/s44443-025-00230-zPGHDR: Dynamic HDR reconstruction with progressive feature alignment and quality-guided fusionYing Qi0Qiushi Li1Zhaoyuan Huang2Jian Li3Chenyang Wang4Teng Wan5Qiang Zhang6School of Computer Science and Engineering, Northwest Normal UniversitySchool of Computer Science and Engineering, Northwest Normal UniversitySchool of Computer Science and Engineering, Northwest Normal UniversitySchool of Computer Science and Engineering, Northwest Normal UniversitySchool of Computer Science and Engineering, Northwest Normal UniversitySchool of Computer Science and Engineering, Northwest Normal UniversitySchool of Computer Science and Engineering, Northwest Normal UniversityAbstract High Dynamic Range (HDR) imaging captures the complete luminance information of a scene by fusing a multi-exposure image sequence. However, a key challenge remains: ghosting artifacts caused by object motion in dynamic scenes. Existing methods typically adopt an align-then-fuse strategy, often overlooking the spatial variability of alignment quality, which makes it difficult to balance ghosting suppression and detail preservation when handling complex motion. To address this issue, this paper proposes PGHDR, which leverages a quality-aware progressive alignment strategy. The core innovation is a learning paradigm that jointly optimizes motion estimation and alignment quality perception. This approach treats alignment quality as a learnable, spatially-varying confidence and materializes this concept through three synergistic modules. The Progressive Deformable Feature Alignment (PDFA) module achieves robust feature extraction through a two-stage deformable convolution and exposure-aware modulation. The Quality-Guided Motion Compensation (QGMC) module jointly learns optical flow and a confidence mask, enabling reliability-aware motion estimation. The Structure-Preserving Adaptive Fusion (SPAF) module performs weighted fusion based on the learned quality information while preserving image details via a local structure constraint. Experiments show that PGHDR achieves a PSNR- $$\ell $$ ℓ of 42.58 dB and an HDR-VDP-2 score of 67.15. Visual evaluations on multiple datasets demonstrate that our method significantly outperforms existing approaches, achieving high-quality, ghost-free HDR reconstruction.https://doi.org/10.1007/s44443-025-00230-zHigh dynamic range (HDR) imagingQuality-aware fusionDeformable convolutionMotion estimationDeep learning |
| spellingShingle | Ying Qi Qiushi Li Zhaoyuan Huang Jian Li Chenyang Wang Teng Wan Qiang Zhang PGHDR: Dynamic HDR reconstruction with progressive feature alignment and quality-guided fusion Journal of King Saud University: Computer and Information Sciences High dynamic range (HDR) imaging Quality-aware fusion Deformable convolution Motion estimation Deep learning |
| title | PGHDR: Dynamic HDR reconstruction with progressive feature alignment and quality-guided fusion |
| title_full | PGHDR: Dynamic HDR reconstruction with progressive feature alignment and quality-guided fusion |
| title_fullStr | PGHDR: Dynamic HDR reconstruction with progressive feature alignment and quality-guided fusion |
| title_full_unstemmed | PGHDR: Dynamic HDR reconstruction with progressive feature alignment and quality-guided fusion |
| title_short | PGHDR: Dynamic HDR reconstruction with progressive feature alignment and quality-guided fusion |
| title_sort | pghdr dynamic hdr reconstruction with progressive feature alignment and quality guided fusion |
| topic | High dynamic range (HDR) imaging Quality-aware fusion Deformable convolution Motion estimation Deep learning |
| url | https://doi.org/10.1007/s44443-025-00230-z |
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