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Diffusion probabilistic model for Tibetan painted sketch extraction.

Fubo Wang^1,2,3, Shengling Geng^4,5,6, Zeyu Jia^1,2,3

¹Qinghai Normal University, School of Computer Science, Xining, 810008, China.

Scientific Reports

|July 16, 2025

Summary

This summary is machine-generated.

DiffusionSketch, a novel method using Diffusion Probabilistic Models (DPM), effectively extracts clear Tibetan painting sketches. This approach significantly improves upon existing methods in both subjective and objective evaluations.

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Area of Science:

Computer Vision
Artificial Intelligence
Digital Art Preservation

Background:

Tibetan paintings require meticulous sketches, often redrawn identically for different artworks.
Extracting these complex sketches is vital but challenging due to intricate details.
Existing methods struggle with the fine lines and colors characteristic of Tibetan art.

Purpose of the Study:

To develop an automated method for extracting high-fidelity sketches from Tibetan paintings.
To address the limitations of current sketch extraction techniques for complex cultural artworks.
To introduce a Diffusion Probabilistic Model (DPM)-based approach for enhanced sketch generation.

Main Methods:

Proposed DiffusionSketch method utilizing DPM for sketch extraction.
Two-stage feature extraction capturing global and local context.
Adaptive wavelet filter and Feature Fusion Module (FFM) for feature integration.
Generative Adversarial Network (GAN) for refining sketch output and minimizing distortions.

Main Results:

DiffusionSketch generates clear, concise, and low-noise Tibetan painting sketches.
The method demonstrates superior performance on the HeHuang Tibetan Painting (HHTP) dataset.
Achieved top ranking among 36 methods, with significant improvements in subjective (57.12%) and objective (18.30%) evaluations.

Conclusions:

DiffusionSketch offers an efficient and effective solution for Tibetan painting sketch extraction.
The DPM-based approach successfully preserves intricate details and artistic quality.
This method provides a valuable tool for digital preservation and replication of cultural art.