ABSTRACT
Mural paintings, as the main components of painted cultural relics, have essential research value and historical significance. Due to their age, murals are easily damaged. Obtaining intact sketches is the first step in the conservation and restoration of murals. However, sketch extraction often suffers from problems such as loss of details, too thick lines, or noise interference. To overcome these problems, a mural sketch extraction method based on image enhancement and edge detection is proposed. The experiments utilize Contrast Limited Adaptive Histogram Equalization (CLAHE) and bilateral filtering to enhance the mural images. This can enhance the edge features while suppressing the noise generated by over-enhancement. Finally, we extract the refined sketch of the mural using the Laplacian Edge with fine noise remover (FNR). The experimental results show that this method is superior to other methods in terms of visual effect and related indexes, and it can extract the complex line regions of the mural.
ABSTRACT
Environmental changes and human activities have caused serious degradation of murals around the world. Scratches are one of the most common issues in these damaged murals. We propose a new method for virtually enhancing and removing scratches from murals; which can provide an auxiliary reference and support for actual restoration. First, principal component analysis (PCA) was performed on the hyperspectral data of a mural after reflectance correction, and high-pass filtering was performed on the selected first principal component image. Principal component fusion was used to replace the original first principal component with a high-pass filtered first principal component image, which was then inverse PCA transformed with the other original principal component images to obtain an enhanced hyperspectral image. The linear information in the mural was therefore enhanced, and the differences between the scratches and background improved. Second, the enhanced hyperspectral image of the mural was synthesized as a true colour image and converted to the HSV colour space. The light brightness component of the image was estimated using the multi-scale Gaussian function and corrected with a 2D gamma function, thus solving the problem of localised darkness in the murals. Finally, the enhanced mural images were applied as input to the triplet domain translation network pretrained model. The local branches in the translation network perform overall noise smoothing and colour recovery of the mural, while the partial nonlocal block is used to extract the information from the scratches. The mapping process was learned in the hidden space for virtual removal of the scratches. In addition, we added a Butterworth high-pass filter at the end of the network to generate the final restoration result of the mural with a clearer visual effect and richer high-frequency information. We verified and validated these methods for murals in the Baoguang Hall of Qutan Temple. The results show that the proposed method outperforms the restoration results of the total variation (TV) model, curvature-driven diffusion (CDD) model, and Criminisi algorithm. Moreover, the proposed combined method produces better recovery results and improves the visual richness, readability, and artistic expression of the murals compared with direct recovery using a triple domain translation network.