- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Joint Color Space GMMs for CFA Demosaicking
摘要： We propose a patch-based algorithm for demosaicking a mosaicked color image produced by color filter arrays commonly used in acquiring color images. The proposed algorithm exploits a joint color space Gaussian mixture model (JCS-GMM) prior for jointly characterizing the patches from red, green, and blue channels of a color image. The inter channel correlations captured by the covariance matrices of Gaussian models are exploited to estimate the pixel values missing in the mosaicked image. The proposed JCS-GMM demosaicking algorithm can be seen as the GMM analogue of the Color-KSVD algorithm, which has produced impressive results in color image denoising and demosaicking. We demonstrate that our proposed algorithm achieves superior performance in the case of Kodak and Laurent Condat’s databases, and competitive performance in the case of IMAX database, when compared with state-of-the-art demosaicking algorithms.
关键词： Color filter array,demosaicking,Bayer pattern,Gaussian mixture models
更新于2025-09-23 15:22:29
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[IEEE 2018 International Symposium in Sensing and Instrumentation in IoT Era (ISSI) - Shanghai, China (2018.9.6-2018.9.7)] 2018 International Symposium in Sensing and Instrumentation in IoT Era (ISSI) - Image Denoising Using Asymmetric Gaussian Mixture Models
摘要： Finite mixture models are widely applied in image denoising because they have sound mathematical basis and the results are interpretable. In a manner of speaking, they can give a mathematical description of natural images by clustering. Usually assume that per-component of natural images follow a mixture of Gaussian(GMM) when doing image denoising. However, it is well- know that most of natural images are intricate and of which the distribution is highly non-Gaussian. So there remain problems that GMM cannot fix. In this paper, we introduce the asymmetric Gaussian mixture models into the finite mixture model, in which GMM is a special case. Asymmetric Gaussian Mixture (AGM) can model asymmetric distribution which is more conform to the data of natural images. We do some experiments in image denoising under different noise scales and types. The AGM can have better results compare to The GMM.
关键词： image denoising,priors,asymmetric Gaussian mixture models,expected patch log likelihood
更新于2025-09-10 09:29:36
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[IEEE 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) - Ostrava (2018.9.17-2018.9.20)] 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) - Supervised Level Sets For Dermoscopic Image Segmentation
摘要： In this paper, we propose a novel segmentation method that has been used for segmenting lesions in dermoscopy images. This method uses the variational level sets formulation with a novel area term based on supervised learning that results in the global optimization of a cost function, that can potentially result in a robust segmentation of the images. This term uses a mixture of Gaussians that are trained from a set of training images, and evolves an active contour such that the difference between the learned models and the empirical distributions obtained from the evolving curve for both the lesion and the skin are minimized. In the end, our approach is validated on the publicly available PH2 dermoscopy imaging dataset and the results show that the proposed method outperforms the other state-of-the-art methods that have been considered in this paper.
关键词： Gaussian mixture models,Variational level sets,Segmentation,Skin cancer,Dermoscopy
更新于2025-09-09 09:28:46