Design And Implementation Of Image Restoration Algorithms Based On RGB-D

RGB-D images are widely used in computer vision and 3D reconstruction fields.However,due to the inherent imaging errors of depth cameras and the interference of external environmental factors,the acquired depth information may suffer from misalignment of edges,noise,depth value jumps,and occlusion voids,among other distortions.These distortions can affect the application of RGB-D images in relevant research fields.In order to broaden the application range of RGB-D images,this thesis studies RGB-D image restoration algorithms based on traditional mathematical models and neural network models,with the specific content as follows:In response to the problem of missing depth and color information in some background areas caused by foreground object occlusion,a RGB-D image restoration algorithm based on traditional mathematical models is designed.Firstly,various filtering methods are employed to denoise the images and ensure the stability of depth values.Then,based on the grayscale distance characteristics of the depth map,the maximum between-class variance threshold segmentation method is used to extract the foreground occluding objects and generate masks.Subsequently,the traditional mathematical model Criminisi algorithm is applied to fill the color and depth images covered by the masks.Finally,the restored point cloud model is fused with the point cloud model formed by the original RGB-D image.Experimental results show that the proposed method achieves good restoration results in relatively simple indoor scenes with respect to texture.In addressing the problem of RGB-D image restoration under complex textures,a RGB-D image restoration model based on generative adversarial networks is designed.In the discriminator,a global and local dual-discriminator design is adopted,encouraging the generator network to produce restoration results with richer local detail textures and more consistent global structures.Random rectangular masks are introduced to enhance the robustness of the restoration model.The neural network’s fitting capability is utilized to learn the relationship between the mask and the background,improving the model’s semantic information.Combining the idea of super-resolution reconstruction,the predictions of multiple networks are fused,and a Wasserstein distance-based loss function design is constructed to address the instability of generative adversarial network training.The generator and discriminator are alternately trained for different numbers of iterations within the same iterative process to accelerate parameter updating.Experimental results demonstrate that the proposed model achieves desirable effects in RGB-D image restoration tasks under complex textures,significantly improving the global structural consistency and local detail richness of the restoration results.