Combined image compression and denoising using wavelets
Image Communication
Denoising by sparse approximation: error bounds based on rate-distortion theory
EURASIP Journal on Applied Signal Processing
Multilevel algorithm for a Poisson noise removal model with total-variation regularization
International Journal of Computer Mathematics - Fast Iterative and Preconditioning Methods for Linear and Non-Linear Systems
Simultaneous MAP-based video denoising and rate-distortion optimized video encoding
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Signal Processing
Noise removal from images by projecting onto bases of principal components
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
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We study a new approach to image denoising based on complexity regularization. This technique presents a flexible alternative to the more conventional l2,l1, and Besov regularization methods. Different complexity measures are considered, in particular those induced by state-of-the-art image coders. We focus on a Gaussian denoising problem and derive a connection between complexity-regularized denoising and operational rate-distortion optimization. This connection suggests the use of efficient algorithms for computing complexity-regularized estimates. Bounds on denoising performance are derived in terms of an index of resolvability that characterizes the compressibility of the true image. Comparisons with state-of-the-art denoising algorithms are given