Srishti 1806 painting in painting proposal#30
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Srishti-1806 wants to merge 4 commits intohumanai-foundation:masterfrom
Open
Srishti 1806 painting in painting proposal#30Srishti-1806 wants to merge 4 commits intohumanai-foundation:masterfrom
Srishti-1806 wants to merge 4 commits intohumanai-foundation:masterfrom
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This proposal enhances the original idea in several key ways: 1. Accessibility and Scalability Unlike traditional methods that require multispectral or X-ray data, this system works with standard RGB images, making it deployable in low-resource environments and scalable across large datasets. 2. Software-first Approach By shifting from hardware-dependent imaging to AI-driven reconstruction, the project reduces cost and increases usability. This allows broader adoption across institutions, researchers, and independent analysts. 3. End-to-End Automated Pipeline The proposed system integrates multiple stages—denoising, segmentation, inpainting, enhancement, and upscaling—into a single cohesive pipeline, reducing manual intervention. 4. Generative Reconstruction instead of Detection Rather than only identifying whether a hidden image exists, this approach attempts to reconstruct plausible underlying visuals, providing richer insights. 5. Deployment-ready Architecture The system is designed with FastAPI and Docker-based deployment, ensuring reproducibility, scalability, and ease of integration into real-world applications.
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This proposal enhances the original idea in several key ways:
https://github.com/Srishti-1806/OS_1_PAINTING_IN_A_PAINTING
USP OF THE SOLUTION (replaced database dependency with Generative AI pipene integrated with ControlNet model (Canny Edge Detection) for refined image generation after segmentation ).
Instead of a static retrieval-based system that is limited by a fixed database, I am proposing a dynamic Generative Reconstruction Pipeline. This not only reduces server-side latency and storage overhead but also ensures the system can generalize to artworks that have never been digitally cataloged before.
Stable Diffusion XL (SDXL) or ControlNet Tile can be integrated to produce high resolution images as part of future developements in the proposed idea.
Key addons:
combined = cv2.bitwise_or(edges, seg_mask) -- in pipeline.py ensures that imprinting is done only where distortion exists or missing pixels exist).
Features:
Accessibility and Scalability
Unlike traditional methods that require multispectral or X-ray data, this system works with standard RGB images, making it deployable in low-resource environments and scalable across large datasets.
Software-first Approach
By shifting from hardware-dependent imaging to AI-driven reconstruction, the project reduces cost and increases usability. This allows broader adoption across institutions, researchers, and independent analysts.
End-to-End Automated Pipeline
The proposed system integrates multiple stages—denoising, segmentation, inpainting, enhancement, and upscaling—into a single cohesive pipeline, reducing manual intervention.
Generative Reconstruction instead of Detection
Rather than only identifying whether a hidden image exists, this approach attempts to reconstruct plausible underlying visuals, providing richer insights.
Deployment-ready Architecture
The system is designed with FastAPI and Docker-based deployment, ensuring reproducibility, scalability, and ease of integration into real-world applications.