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Zenodo
2026
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| Online-Zugang: | https://doi.org/10.5281/zenodo.19645829 |
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| _version_ | 1866901225480388608 |
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| author | Chadalavada, Hanuma Ramesh |
| author_facet | Chadalavada, Hanuma Ramesh |
| contents | <p>Generative image editing models apply holistic scene-level transformations but systematically degrade semantically structured regions (text, logos, fine patterns) whose fidelity depends on high-frequency detail preservation. We formalize this as context-preserving semantic restoration (CPSR): given an original image and a generatively edited image, recover a result satisfying dual objectives of semantic fidelity on structured regions and context preservation on the scene. General-purpose models (Gemini, FLUX Pro Edit) fail because they lack the domain-specific inverse mapping from artifact space to ground-truth content. We address this via a synthetic degradation curriculum generating paired training data at three severity levels enabling LoRA adaptation (15-25 MB, 1000 steps), a segmentation-guided dual-inference architecture with parallel per-keyword SAM 3 detection, ECC sub-pixel alignment, and feathered mask compositing, and mode-conditioned restoration policies for five editing modalities. Evaluated on 120 images across 8 product lines, our approach achieves 89% text legibility (vs 52% FLUX Pro Edit, 41% Gemini), 0.87 SSIM on device regions, and less than 0.02 LPIPS context deviation. We discuss generalization to automotive, pharmaceutical, retail, and architectural domains.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19645829 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | LoRA-Guided Semantic Restoration for Generative Image Editing Artifacts Chadalavada, Hanuma Ramesh <p>Generative image editing models apply holistic scene-level transformations but systematically degrade semantically structured regions (text, logos, fine patterns) whose fidelity depends on high-frequency detail preservation. We formalize this as context-preserving semantic restoration (CPSR): given an original image and a generatively edited image, recover a result satisfying dual objectives of semantic fidelity on structured regions and context preservation on the scene. General-purpose models (Gemini, FLUX Pro Edit) fail because they lack the domain-specific inverse mapping from artifact space to ground-truth content. We address this via a synthetic degradation curriculum generating paired training data at three severity levels enabling LoRA adaptation (15-25 MB, 1000 steps), a segmentation-guided dual-inference architecture with parallel per-keyword SAM 3 detection, ECC sub-pixel alignment, and feathered mask compositing, and mode-conditioned restoration policies for five editing modalities. Evaluated on 120 images across 8 product lines, our approach achieves 89% text legibility (vs 52% FLUX Pro Edit, 41% Gemini), 0.87 SSIM on device regions, and less than 0.02 LPIPS context deviation. We discuss generalization to automotive, pharmaceutical, retail, and architectural domains.</p> |
| title | LoRA-Guided Semantic Restoration for Generative Image Editing Artifacts |
| url | https://doi.org/10.5281/zenodo.19645829 |