Multi-focus image fusion using adaptive patch rendering anisotropic diffusion filter
Abstract Multi-focus image fusion (MFIF) extracts various focused regions from partially focused images of the same scene which are subsequently merged to create a composite image in which all objects are visualised precisely. Because of the diffusion of spatial intensities at the edges, many conven...
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-97323-6 |
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| author | Sandhya Tatekalva G. Tirumala Vasu Samreen Fiza Hanumantharao Bitra |
| author_facet | Sandhya Tatekalva G. Tirumala Vasu Samreen Fiza Hanumantharao Bitra |
| author_sort | Sandhya Tatekalva |
| collection | DOAJ |
| description | Abstract Multi-focus image fusion (MFIF) extracts various focused regions from partially focused images of the same scene which are subsequently merged to create a composite image in which all objects are visualised precisely. Because of the diffusion of spatial intensities at the edges, many conventional MFIF methods face difficulties with respect to spatially inconsistent structures, visual distortion, ghost artifacts, and preserving edge information in the fused image. To address these issues, we proposed the Adaptive Patch Rendering Anisotropic Diffusion Filter (APRADF) for MFIF. To acquire large- and small-scale intensity variations, we first decompose the two source images into a base layer and a detail layer. We further capture the focus and blur patches of the source images to detect the diffusion of the edges at the boundaries. The proposed APRADF is then applied on the weight maps, base layer and detail layer. At last, the final fused image will be produced by adding the fused detail layer to the fused base layer linearly. Both qualitative and quantitative investigations were conducted on publicly accessible databases to evaluate the performance of the proposed method. The experimental results reveal that APRADF-based fusion outperforms state-of-the-art algorithms in subjective and objective analysis. |
| format | Article |
| id | doaj-art-2b16b422100a45b8a023b5c13fb0eb08 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-2b16b422100a45b8a023b5c13fb0eb082025-08-20T01:53:18ZengNature PortfolioScientific Reports2045-23222025-05-0115111610.1038/s41598-025-97323-6Multi-focus image fusion using adaptive patch rendering anisotropic diffusion filterSandhya Tatekalva0G. Tirumala Vasu1Samreen Fiza2Hanumantharao Bitra3Department of Computer Science, SVU College of CM & CS, S. V. UniversityInnovation & Translational Research Hub (Itrh), Department of Electronics & Communications Engineering, Presidency UniversityDepartment of Electronics & Communications Engineering, Presidency UniversitySchool of Electronics Engineering, VIT-AP UniversityAbstract Multi-focus image fusion (MFIF) extracts various focused regions from partially focused images of the same scene which are subsequently merged to create a composite image in which all objects are visualised precisely. Because of the diffusion of spatial intensities at the edges, many conventional MFIF methods face difficulties with respect to spatially inconsistent structures, visual distortion, ghost artifacts, and preserving edge information in the fused image. To address these issues, we proposed the Adaptive Patch Rendering Anisotropic Diffusion Filter (APRADF) for MFIF. To acquire large- and small-scale intensity variations, we first decompose the two source images into a base layer and a detail layer. We further capture the focus and blur patches of the source images to detect the diffusion of the edges at the boundaries. The proposed APRADF is then applied on the weight maps, base layer and detail layer. At last, the final fused image will be produced by adding the fused detail layer to the fused base layer linearly. Both qualitative and quantitative investigations were conducted on publicly accessible databases to evaluate the performance of the proposed method. The experimental results reveal that APRADF-based fusion outperforms state-of-the-art algorithms in subjective and objective analysis.https://doi.org/10.1038/s41598-025-97323-6Multi-focus image fusionAdaptive patch rendering anisotropic diffusion filterImage decompositionBase layerDetail layerImage fusion quality metrics |
| spellingShingle | Sandhya Tatekalva G. Tirumala Vasu Samreen Fiza Hanumantharao Bitra Multi-focus image fusion using adaptive patch rendering anisotropic diffusion filter Scientific Reports Multi-focus image fusion Adaptive patch rendering anisotropic diffusion filter Image decomposition Base layer Detail layer Image fusion quality metrics |
| title | Multi-focus image fusion using adaptive patch rendering anisotropic diffusion filter |
| title_full | Multi-focus image fusion using adaptive patch rendering anisotropic diffusion filter |
| title_fullStr | Multi-focus image fusion using adaptive patch rendering anisotropic diffusion filter |
| title_full_unstemmed | Multi-focus image fusion using adaptive patch rendering anisotropic diffusion filter |
| title_short | Multi-focus image fusion using adaptive patch rendering anisotropic diffusion filter |
| title_sort | multi focus image fusion using adaptive patch rendering anisotropic diffusion filter |
| topic | Multi-focus image fusion Adaptive patch rendering anisotropic diffusion filter Image decomposition Base layer Detail layer Image fusion quality metrics |
| url | https://doi.org/10.1038/s41598-025-97323-6 |
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