Clipped noisy images: Heteroskedastic modeling and practical denoising
Signal Processing
Patch-based video processing: a variational Bayesian approach
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Image Processing
From Local Kernel to Nonlocal Multiple-Model Image Denoising
International Journal of Computer Vision
Image estimation using total least squares
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Journal of Computational and Applied Mathematics
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
Low-light imaging method with visible-band and wide-band image pair
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Online algorithm based on support vectors for orthogonal regression
Pattern Recognition Letters
Simplified noise model parameter estimation for signal-dependent noise
Signal Processing
Joint image denoising using adaptive principal component analysis and self-similarity
Information Sciences: an International Journal
A New Poisson Noise Filter Based on Weights Optimization
Journal of Scientific Computing
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In this paper, we present a method for removing noise from digital images corrupted with additive, multiplicative, and mixed noise. An image patch from an ideal image is modeled as a linear combination of image patches from the noisy image. We propose to fit this model to the real-world image data in the total least square (TLS) sense, because the TLS formulation allows us to take into account the uncertainties in the measured data. We develop a method to reduce the contribution from the irrelevant image patches, which will sharpen the edges and reduce edge artifacts at the same time. Although the proposed algorithm is computationally demanding, the image quality of the output image demonstrates the effectiveness of the TLS algorithms