Name
Abstract | ||
|---|---|---|
We propose a data-dependent denoising procedure to restore noisy images. Different from existing denoising algorithms which search for patches from either the noisy image or a generic database, the new algorithm finds patches from a database that contains relevant patches. We formulate the denoising problem as an optimal filter design problem and make two contributions. First, we determine the basis function of the denoising filter by solving a group sparsity minimization problem. The optimization formulation generalizes existing denoising algorithms and offers systematic analysis of the performance. Improvement methods are proposed to enhance the patch search process. Second, we determine the spectral coefficients of the denoising filter by considering a localized Bayesian prior. The localized prior leverages the similarity of the targeted database, alleviates the intensive Bayesian computation, and links the new method to the classical linear minimum mean squared error estimation. We demonstrate applications of the proposed method in a variety of scenarios, including text images, multiview images, and face images. Experimental results show the superiority of the new algorithm over existing methods. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TIP.2015.2414873 | IEEE Transactions on Image Processing |
Keywords | DocType | Volume |
bm3d,adaptive image denoising algorithm,localized bayesian prior,patch-based filtering,bayesian estimation,face imaging,noisy image restoration,spectral coefficient,optimization formulation,multiview imaging,targeted database,image denoising,image restoration,intensive bayesian computation,data-dependent denoising procedure,search problems,least mean squares methods,patch search process,bayes methods,non-local means,text imaging,filtering theory,optimal filter design problem,external database,minimisation,image enhancement,group sparsity minimization problem,classical linear minimum mean squared error estimation,optimal filter,group sparsity,tensile stress,databases,noise reduction,non local means,principal component analysis,optimization,noise measurement | Journal | 24 |
Issue | ISSN | Citations |
7 | 1941-0042 | 30 |
PageRank | References | Authors |
0.83 | 48 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Enming Luo | 1 | 84 | 6.63 |
| Stanley H. Chan | 2 | 403 | 30.95 |
| Truong Q. Nguyen | 3 | 1402 | 136.69 |