Clustering-based denoising with locally learned dictionaries
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Optimal Spatial Adaptation for Patch-Based Image Denoising
IEEE Transactions on Image Processing
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
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 study the bias characteristics of image denoising algorithms. Recently introduced state-of-the-art denoising methods produce biased estimates of pixel intensities. The bias in each case is dependent on the underlying image geometry. Hence, we cluster the image into groups of patches that share a common underlying structure and study the bias independently in each cluster. We show that the bias in each cluster can be modeled effectively by an affine function, where the parameters of the model differ between clusters and algorithms. We validate our model through experimental results, both visually and quantitatively.