Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
International Journal of Computer Vision
Adaptive wavelet thresholding for image denoising and compression
IEEE Transactions on Image Processing
A joint inter- and intrascale statistical model for Bayesian wavelet based image denoising
IEEE Transactions on Image Processing
Image denoising using scale mixtures of Gaussians in the wavelet domain
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
IEEE Transactions on Image Processing
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
IEEE Transactions on Image Processing
Sparse Representation for Color Image Restoration
IEEE Transactions on Image Processing
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Many state-of-the-art denoising algorithms focus on recovering high-frequency details in noisy images. However, images corrupted by large amounts of noise are also degraded in the lower frequencies. Thus properly handling all frequency bands allows us to better denoise in such regimes. To improve existing denoising algorithms we propose a meta-procedure that applies existing denoising algorithms across different scales and combines the resulting images into a single denoised image. With a comprehensive evaluation we show that the performance of many state-of-the-art denoising algorithms can be improved.