Abstract
We study a new approach to image denoising based on complexity regularization. This technique presents a flexible alternative to the more conventional l 2, l 1, 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.
Original language | English (US) |
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Pages (from-to) | 841-851 |
Number of pages | 11 |
Journal | IEEE Transactions on Image Processing |
Volume | 10 |
Issue number | 6 |
DOIs | |
State | Published - Jun 2001 |
Keywords
- Image compression
- Image restoration
- Minimum description length principle
- Rate-distortion optimization
- Regularization
- Wavelets
ASJC Scopus subject areas
- Software
- Computer Graphics and Computer-Aided Design