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17 December 2015 Image denoising by principal basis analysis
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Proceedings Volume 9811, MIPPR 2015: Multispectral Image Acquisition, Processing, and Analysis; 98110K (2015)
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China
This article addresses the image denoising problem in the situations of strong noise. The method we propose is intended to preserve faint signal details under these difficult circumstances. The new method we introduce, called principal basis analysis, is based on a novel criterion: the reproducibility which is an intrinsic characteristic of the geometric regularity in natural images. We show how to measure reproducibility. Then we present the principal basis analysis method, which chooses, in sparse representation of the signal, the components optimizing the reproducibility degree to build a so-called principal basis. With this principal basis, we show that a noise-free reconstruction may be obtained. As illustrations, we apply the principal signal basis to image denoising for natural images with details in low signal-to-noise ratio, showing performance better than some reference methods.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hong Sun, Cheng-Wei Sang, and Cheng-Guang Liu "Image denoising by principal basis analysis", Proc. SPIE 9811, MIPPR 2015: Multispectral Image Acquisition, Processing, and Analysis, 98110K (17 December 2015);


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