期刊
IEEE TRANSACTIONS ON IMAGE PROCESSING
卷 28, 期 9, 页码 4247-4259出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2019.2907478
关键词
Color image denoising; multispectral image denoising; non-local filters; transform domain techniques; block diagonal representation
资金
- National Natural Science Foundation of China [61273295]
- Science and Technology Program of Guangzhou [201607010069]
Filtering images of more than one channel are challenging in terms of both efficiency and effectiveness. By grouping similar patches to utilize the self-similarity and sparse linear approximation of natural images, recent nonlocal and transform-domain methods have been widely used in color and multispectral image (MSI) denoising. Many related methods focus on the modeling of group level correlation to enhance sparsity, which often resorts to a recursive strategy with a large number of similar patches. The importance of the patch level representation is understated. In this paper, we mainly investigate the influence and potential of representation at patch level by considering a general formulation with a block diagonal matrix. We further show that by training a proper global patch basis, along with a local principal component analysis transform in the grouping dimension, a simple transform-threshold-inverse method could produce very competitive results. Fast implementation is also developed to reduce the computational complexity. The extensive experiments on both the simulated and real datasets demonstrate its robustness, effectiveness, and efficiency.
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