Abstract
We address the image denoising problem, where zero-mean white and homogeneous Gaussian additive noise is to be removed from a given image. The approach taken is based on sparse and redundant representations over trained dictionaries. Using the K-SVD algorithm, we obtain a dictionary that describes the image content effectively. Two training options are considered: using the corrupted image itself, or training on a corpus of high-quality image database. Since the K-SVD is limited in handling small image patches, we extend its deployment to arbitrary image sizes by defining a global image prior that forces sparsity over patches in every location in the image. We show how such Bayesian treatment leads to a simple and effective denoising algorithm. This leads to a state-of-the-art denoising performance, equivalent and sometimes surpassing recently published leading alternative denoising methods.
Keywords
Artificial intelligenceSparse approximationNoise reductionPattern recognition (psychology)Image (mathematics)Computer scienceNon-local meansK-SVDVideo denoisingImage restorationImage denoisingImage qualityImage processingComputer visionMathematicsVideo processing
Affiliated Institutions
Related Publications
Image Super-Resolution Via Sparse Representation
This paper presents a new approach to single-image super-resolution, based on sparse signal representation. Research on image statistics suggests that image patches can be well-...
Beta Process Joint Dictionary Learning for Coupled Feature Spaces with Application to Single Image Super-Resolution
This paper addresses the problem of learning over-complete dictionaries for the coupled feature spaces, where the learned dictionaries also reflect the relationship between the ...
Atomic Decomposition by Basis Pursuit
The time-frequency and time-scale communities have recently developed a large number of overcomplete waveform dictionaries --- stationary wavelets, wavelet packets, cosine packe...
Momentum Contrast for Unsupervised Visual Representation Learning
We present Momentum Contrast (MoCo) for unsupervised visual representation learning. From a perspective on contrastive learning as dictionary look-up, we build a dynamic diction...
SwinIR: Image Restoration Using Swin Transformer
Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy and compressed images). W...
Publication Info
- Year
- 2006
- Type
- article
- Volume
- 15
- Issue
- 12
- Pages
- 3736-3745
- Citations
- 5309
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
5309
OpenAlex
Cite This
Michael Elad,
Michal Aharon
(2006).
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries.
IEEE Transactions on Image Processing
, 15
(12)
, 3736-3745.
https://doi.org/10.1109/tip.2006.881969
Identifiers
- DOI
- 10.1109/tip.2006.881969