Automatic partitioning of full-motion video
Readings in multimedia computing and networking
The Nonlinear Statistics of High-Contrast Patches in Natural Images
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
Combined Wavelet Domain and Temporal Video Denoising
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
A Non-Local Algorithm for Image Denoising
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Video shot segmentation using singular value decomposition
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Nonlocal Image and Movie Denoising
International Journal of Computer Vision
Bayesian non-local means filter, image redundancy and adaptive dictionaries for noise removal
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Fast non local means denoising for 3d MR images
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
IEEE Transactions on Image Processing
Performance characterization of video-shot-change detection methods
IEEE Transactions on Circuits and Systems for Video Technology
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Nonlocal means (NLM) video denoising algorithm though provide very competitive results, suffer from high computational cost. We propose to reduce the computations through the concept of dimensionality reduction using principle component analysis (PCA). Image neighbourhood representations are projected onto a lower dimensional subspace determined by PCA and weights are computed in this reduced subspace. Principle components are computed globally for an entire video shot having similar frames, which reduces computations drastically. We have used a technique of histogram difference to group the frames with similar visual content. We have achieved an improvement in accuracy in addition to reducing the computation. The proposed method is shown to outperform all other nonlocal means related video denoising methods.