Depth image based rendering (DIBR) is the most widely used technology among synthesis algorithms. Hole filling is a challenge in producing desirable synthesized images. In this paper, we propose an enhanced non local mean based hole filling method. Color, gradient and depth information is combined to select the optimal candidate patches. The missing information from holes is then formed by aggregating multiple candidate patches. Furthermore, an efficient invalid pixel classification method based on their chararcteristics is proposed to divide invalid pixels into three types, and use different methods to fill them, and reduce the computational load of the hole filling unit. The results show that the proposed method has a better robustness and performance for hole filling in DIBR systems than other hole filling based on algorithms.
Disparity refinement based on a regression model continues to be challenging for specified function with a weak generalization ability. An invalid disparity refinement method to improve the quality of the disparity map is proposed. This method includes two committed steps: outliers disparity removal and redefinition of invalid pixels. To obtain a more accurate initial disparity map, removal of outliers based on Chauvenet’s criterion method is proposed, using the distribution of disparity values on a segmentation region. Then, the least square support vector machine model is applied to every horizontal line of the obtained initial disparity map to model the valid disparity values, corresponding image color values, and co-ordinates of pixels. Finally, invalid pixels are redefined by the regression model. Experimental results demonstrate that the dense disparity maps of the proposed method show superior performance compared with current state-of-the-art methods.