کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
529838 | 869716 | 2013 | 6 صفحه PDF | دانلود رایگان |

Redundant dictionary learning based image noise reduction methods explore the sparse prior of patches and have proved to lead to state-of-the-art results; however, they do not explore the non-local similarity of image patches. In this paper we exploit both the structural similarities and sparse prior of image patches and propose a new dictionary learning and similarity regularization based image noise reduction method. By formulating the image noise reduction as a multiple variables optimization problem, we alternately optimize the variables to obtain the denoised image. Some experiments are taken on comparing the performance of our proposed method with its counterparts on some benchmark natural images, and the superiorities of our proposed method to its counterparts can be observed in both the visual results and some numerical guidelines.
► Image denoising.
► Sparse representation.
► Dictionary learning.
► Non-local similarity regularizer.
Journal: Journal of Visual Communication and Image Representation - Volume 24, Issue 2, February 2013, Pages 181–186