Research on Virtual Color Restoration of Complex Building System Based on Discrete Wavelet Transform

Chunqiao Song,Xutong Wu,Gengxin Sun

doi:10.1155/2022/6450780

Abstract

At present, image restoration has become a research hotspot in computer vision. The purpose of digital image restoration is to restore the lost information of the image or remove redundant objects without destroying the integrity and visual effects of the image. The operation of user interactive color migration is troublesome, resulting in low efficiency. And, when there are many kinds of colors, it is prone to errors. In response to these problems, this paper proposes automatic selection of sample color migration. Considering that the respective gray-scale histograms of the visual source image and the target image are approximately normal distributions, this paper takes the peak point as the mean value of the normal distribution to construct the objective function. We find all the required partitions according to the user’s needs and use the center points in these partitions as the initial clustering centers of the fuzzy C-means (FCM) algorithm to complete the automatic clustering of the two images. This paper selects representative pixels as sample blocks to realize automatic matching of sample blocks in the two images and complete the color migration of the entire image. We introduced the curvature into the energy functional of the p-harmonic model. According to whether there is noise in the image, a new wavelet domain image restoration model is proposed. According to the established model, the Euler–Lagrange equation is derived by the variational method, the corresponding diffusion equation is established, and the model is analyzed and numerically solved in detail to obtain the restored image. The results show that the combination of image sample texture synthesis and segmentation matching method used in this paper can effectively solve the problem of color unevenness. This not only saves the time for mural restoration but also improves the quality of murals, thereby achieving more realistic visual effects and connectivity.

Highlights

At present, image restoration has become a research hotspot in computer vision. e purpose of digital image restoration is to restore the lost information of the image or remove redundant objects without destroying the integrity and visual effects of the image. e operation of user interactive color migration is troublesome, resulting in low efficiency
We find all the required partitions according to the user’s needs and use the center points in these partitions as the initial clustering centers of the fuzzy C-means (FCM) algorithm to complete the automatic clustering of the two images. is paper selects representative pixels as sample blocks to realize automatic matching of sample blocks in the two images and complete the color migration of the entire image
Introduction roughout the development of visual art history, from graffiti in prehistoric caves to exquisite murals in churches, from meticulous paintings to fully reproduced photography, every revolutionary change in the reproduction of reality has produced various studies in the field of humanities [1, 2]. e revolutionary changes brought by computer graphics technology in the audio-visual field affected the field of cultural heritage protection and dissemination for the first time

Summary

Related Work

In order to solve the problem of repairing large-scale damage, people have explored texture-based image repair technology [17]. is technology has a good repair effect for images with large damaged areas, especially for complex texture damage. e texture-based image inpainting technology roughly includes the following two types: one is a sample-based texture synthesis model, which mainly includes a texture synthesis algorithm based on nonparametric sampling [18]; the other is a repair model based on image decomposition, including TV decomposition algorithm, wavelet decomposition algorithm, and so on [19]. e model first decomposes the damaged image preprocessing method into two parts: structure and texture, repairs them with different algorithms according to the characteristics of each part, and reconstructs the repaired two parts. In order to reduce the repair time, related scholars have proposed a fast PDE model algorithm, which spreads smooth estimation along the gradient direction of the damaged boundary and constructs the image information into a horizontal set [25]. Rough the normal distribution partition search method, the initial cluster centers required by the FCM algorithm have been obtained, and the set of them is represented by Ci We can obtain the conditions to be met by the cluster center that minimizes the objective function value and the conditions to be met by the degree of membership: ci 􏽑􏽑nj − 0nj1 −􏼐011􏼐−xjxujmij􏼑􏼑umij ,. As the algorithm flexibility parameter, m should be the most ideal. e algorithm will output the cluster center point, the cluster center represents the average attribute of the cluster, and it will output a membership matrix, through which each data object can be distinguished to which cluster each data object belongs. e algorithm is ideal for clustering data objects that meet the normal distribution

Image Restoration Strategy in the Wavelet Domain

Experimental Results and Analysis

Evaluation of the Color Restoration Effect of Cultural Heritage Buildings