- 标题
- 摘要
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- 实验方案
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Quaternion-based weighted nuclear norm minimization for color image denoising
摘要： The quaternion method plays an important role in color image processing, because it represents the color image as a whole rather than as a separate color space component, thus naturally handling the coupling among color channels. The weighted nuclear norm minimization (WNNM) scheme assigns different weights to different singular values, leading to more reasonable image representation method. In this paper, we propose a novel quaternion weighted nuclear norm minimization (QWNNM) model and algorithm under the low rank sparse framework. The proposed model represents the color image as a low rank quaternion matrix, where quaternion singular value decomposition can be calculated by its equivalent complex matrix. We solve the QWNNM by adaptively assigning different singular values with different weights. Color image denoising is implemented by QWNNM based on non-local similarity priors. In this new color space, the inherent color structure can be well preserved during image reconstruction. For high noise levels, we apply a Gaussian lowpass filter (LPF) to the noisy image as a preprocessing before QWNNM, which reduces the iteration numbers and improves the denoised results. The experimental results clearly show that the proposed method outperforms K-SVD, QKSVD and WNNM in terms of both quantitative criteria and visual perceptual.
关键词： Quaternion singular value decomposition,Non-local similarity priors,Quaternion weighted nuclear norm minimization,Low rank,Color image denoising
更新于2025-09-23 15:23:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Local Similarity Regularized Sparse Representation for Hyperspectral Image Super-Resolution
摘要： Recently, performance of hyperspectral image super-resolution (SR) has been significantly improved via sparse representation. However, most of these existing methods fail to consider the local geometrical structure of the sparse coefficients. To take this crucial issue into account, this paper proposes an effective method, which exploits the location related constraint about the sparse coefficients and incorporates their local similarity into the sparse coding process. Thus, the proposed method can preserve the properties of the aforementioned local geometrical structures. Based on the experimental results, the proposed method is demonstrated to be more effective than previous efforts in the task of hyperspectral image SR.
关键词： Local similarity,Sparse representation,Hyperspectral image,Super-resolution
更新于2025-09-04 15:30:14