Reconstruction of Partially Damaged Face Images Based on a Morphable Face Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust De-noising by Kernel PCA
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
The Perceptual Organization of Texture Flow: A Contextual Inference Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital photography with flash and no-flash image pairs
ACM SIGGRAPH 2004 Papers
Flash photography enhancement via intrinsic relighting
ACM SIGGRAPH 2004 Papers
A Statistical Method for Robust 3D Surface Reconstruction from Sparse Data
3DPVT '04 Proceedings of the 3D Data Processing, Visualization, and Transmission, 2nd International Symposium
Robust 3D Reconstruction with Outliers Using RANSAC Based Singular Value Decomposition
IEICE - Transactions on Information and Systems
Handbook of Image and Video Processing (Communications, Networking and Multimedia)
Handbook of Image and Video Processing (Communications, Networking and Multimedia)
Fuzzy peer groups for reducing mixed Gaussian-impulse noise from color images
IEEE Transactions on Image Processing
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In this paper, we propose a denoising method based on PCA reconstruction for removing complex color noise components on human faces, which is not easy to remove by using vectorial color filters. The proposed method is composed of the following six steps: training of canonical eigenface space using PCA, automatic extraction of facial features using active appearance model and alignment of the input face to mean shape, reconstruction of an initial noise free face, relighting of reconstructed face using a bilateral filter, extraction of noise regions using the variances of skin color of training data, and reconstruction using partial information of input images (except the noise regions) and blending of the reconstructed image with the original image. Experimental results show that the proposed denoising method maintains the structural characteristics of input faces, while efficiently removing noise components with complex colors.