Sustainable Restoration of Ancient Architectural Patterns in Fujian Using Improved Algorithms Based on Criminisi

Based on current manual restoration methods, a better algorithm for restoring images based on sample blocks is proposed, along with a sustainable restoration technique for digital virtualization, with the aim of preserving and restoring the priceless art of ancient architectural motifs. The paper uses curve fitting to pre-process the restored photos by re-constructing their damaged borders and filling in the structural information that is absent with the aid of an enhancement of the Criminisi method. The repaired photos have improved edges that were previously blurry, fractured, and over-extended. In order to increase the dependability of the priority calculation when restoring photos and make it possible to acquire a more precise restoration order, we rewrote the priority calculation formula for restoration blocks in the Criminisi algorithm. The purpose was to enhance the aesthetics of the photographs and provide a viable and sustainable restoration technique for the restoration of ancient architectural motifs in Fujian. The Criminisi algorithm with deep learning is used in the thesis to fully restore the content, color, and texture of the architectural photographs, bringing the murals as close to their original state as is practical. In order to improve the blurry, broken, and over-extended edges of the restored images, the broken edges of the images are first repaired through image pre-processing. Then, adjustment factors are added to the priority calculation to increase the weight of the data items, resulting in a more accurate priority order while preventing the priority values from degrading quickly in the later stages of restoration. The PSNR values of the restored images were calculated and compared to those of the Criminisi method, demonstrating that the revised algorithm produces better restoration results and can effectively improve restoration efficiency while lowering restoration costs and ensuring pattern restoration sustainability. By retaining as much of the structural information of the original image as possible in the design of the network model and allocating larger weights to the structural part, this process also uses style migration in deep learning to restore the texture and color of the mural. As a result, the final image is as similar to the original image as possible in terms of content and as similar as possible to the style image in terms of color and texture. A better solution is proposed based on the Criminisi algorithm. By comparing the experimental results of the three sets of building images, the PSNR values of the priority improvement algorithm (30.26, 38.06, 39.56) were significantly better than those of the Criminisi algorithm (27.59, 37.06, 37.59), using the peak signal-to-noise ratio (PSNR) values as a reference standard. In order to determine the appropriate restoration sequence and enhance the quality of picture repair, the broken edges of the pattern are strengthened. The algorithm’s matching criteria can be applied in subsequent work to improve sample-matching accuracy and produce better sustainable restoration results for ancient architectural patterns in Fujian. It no longer requires specialized professional knowledge to reproduce the color of faded architectural photos; instead, a style migration approach is employed to recover the color and texture of architectural images. This study proposes the use of a texture synthesis method and a layered processing method through which the PSNR values of the resulting restored images calculated are superior and significantly higher than those of the sample-based method and the variational framework of synthetic images with regular texture components. We achieved the creation of an updated Criminisi algorithm-based solution that improves the quality of image restoration by fortifying the pattern’s frayed edges and determining the optimum repair order. These two techniques can be combined to improve the sustainability of restoration of faded architectural photographs for issues such as pattern breakage, color loss and fading. To achieve better restoration results for the historic architectural patterns in Fujian, the accuracy of sample matching can be increased, starting with the algorithm’s matching criterion