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Due to the fact that murals are usually displayed on a large area, it is necessary to develop intelligent algorithms for high-resolution images. In recent years, deep learning has been widely applied in the field of image processing. For the problem of high-resolution image restoration in murals, deep learning technology can also be used to solve it. This article carries out systematic research on deep learning-driven high-resolution image restoration for murals from the perspective of vision sensing. Firstly, principal characteristics of mural paintings such as textures and structures are extracted using conventional vision feature representation. Then, the extracted feature contents are mapped into restorage schemes with the assistance of deep neural network structure, so that digital restoration of mural paintings can be realized. The proposed solution blocks high priority sample blocks, prevents sample blocks with a large number of unknown pixels from being processed, and reduces the continuous accumulation of errors caused by matching errors to achieve digital restoration of murals. The simulation results on real-world image sets show that compared to the baseline method, the recovery accuracy can be improved by more than 20%. This method can restore the main structure and texture details of complex scene images, especially in the case of large-scale information loss. |
