Wavelet-Based Dual-Stream Network for Inpainting of Dunhuang Murals

Abstract The preservation and restoration of the Dunhuang murals, a paramount element of China's ancient artistic heritage, are confronted with significant challenges due to their extensive degradation over centuries. Traditional inpainting methods, which are mainly focused on the spatial domain, frequently fall short in authentically restoring these murals, especially considering their distinct artistic style. In response to these challenges, our study introduces a novel wavelet-based dual-stream network (WDSN). This innovative approach bifurcates the restoration process into distinct structure and texture pathways, employing wavelet decomposition to dissect the damaged image into low-frequency (structural) and high-frequency (textural) subbands. A key feature of our method is the incorporation of an Efficient Transformer Block (ETB) within the structure branch, alongside a Residual in Residual Dense Block (RRDB) in the texture branch. These elements synergistically enhance the fidelity of global structural reconstruction and local texture refinement. Furthermore, the integration of the CIEDE2000 color difference formula plays a pivotal role in ensuring the color authenticity of the restored murals, aligning more closely with human visual perception. Our comprehensive tests indicate that this methodology substantially outperforms existing algorithms in mural inpainting, offering a groundbreaking solution for the restoration and preservation of these invaluable cultural artifacts, thereby contributing significantly to the conservation of global art heritage.