Computer-Aided Mural Digital Restoration under Generalized Regression Neural Network

Aiming at the digital protection of classical murals and according to the method of generalized regression neural network (GRNN), a digital restoration is proposed in the paper. Firstly, the existing defect photos are processed preliminarily, including the elimination of noise, the extraction of boundary pixels of the region to be repaired, and the establishment of several small block regions centered on these pixels. Then, similar known pixel regions are found as sample pixel blocks, which are used as input samples of GRNN. Finally, the GRNN is adopted to obtain the approximate estimation function, and the adaptive smoothing parameters are introduced to obtain the pixel information of the area to be repaired. Through model prediction, by acquiring the pixel information of the area to be repaired, the damaged area of the original image can be repaired. The method proposed is compared with the traditional repair methods, the results show that the method is close to the texture structure image restoration method in peak signal-to-noise ratio, and the restoration results are in line with the expectation.