Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
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
Random Walks for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering-based denoising with locally learned dictionaries
IEEE Transactions on Image Processing
Fast non-local algorithm for image denoising
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Markovian clustering for the non-local means image denoising
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
IEEE Transactions on Image Processing
Stochastic image denoising based on Markov-chain Monte Carlo sampling
Signal Processing
Random walks, constrained multiple hypothesis testing and image enhancement
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Adaptive wavelet thresholding for image denoising and compression
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Kernel Regression for Image Processing and Reconstruction
IEEE Transactions on Image Processing
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
IEEE Transactions on Image Processing
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In this paper, we propose an iterative approach for image denoising using random sampling and 3-D transforms. To denoise an image block first we form an array of similar blocks followed by a sparse 3-D transform. Thresholding in 3-D transform domain followed by non-local means approach reconstructs the denoised image block. By processing all overlapping blocks and aggregating them using suitable weights we obtain the denoised estimate. To measure the completeness of the denoising process we apply a robust median estimator to estimate the output noise. The above steps are iterated till the output noise is minimum. By comparing with the state-of-the-art algorithms, we show that the proposed method outperforms the competing algorithms in terms of PSNR, structural similarity index and visual similarity.