Asymmetric Kernel Regression

Mackenzie, M.

doi:10.1109/tnn.2004.824414

Cited by 25 publications

(11 citation statements)

References 25 publications

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“…The proposed SR method can be naturally extended to further improve the performance by incorporating other effective priors such as edge prior [17], steering kernel regression (SKR) [12], and total variation (TV).…”

Section: Resultsmentioning

confidence: 99%

Image Super-resolution Based on Sparse Representation With Joint Constraints

Deng

Liu

et al. 2014

Proceedings of International Conference on Internet Multimedia Computing and Service

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Learning-based image super-resolution (SR) methods are prone to introduce artifacts into resultant high-resolution (HR) images, while reconstruction-based ones tend to blur fine-grained parts and result in unnatural results. To solve these problems, this paper proposes a novel image SR algorithm based on sparse representation with joint constraints. The self-similar redundancy structure in input LR image is learned to construct sparse representation dictionary. Sparse representation, gradient histogram preservation (GHP) and non-local means (NLMs) are incorporated into a maximum a posterior (MAP) estimation framework. Then, we gradually magnify the LR input in the current frame to the desired size and obtain a high-quality SR image. Experimental results demonstrate that the proposed method outperforms the state-of-the-arts.

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Section: Resultsmentioning

confidence: 99%

Image Super-resolution Based on Sparse Representation With Joint Constraints

Deng

Liu

et al. 2014

Proceedings of International Conference on Internet Multimedia Computing and Service

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“…The use of asymmetric kernel distances is not a recent idea but, rather, a well-matured topic Kulis et al (2011); Mackenzie and Tieu (2004); Tsuda (1999); Wua et al (2010). In Tsuda (1999) asymmetric kernels are proposed in the context of SVM classification.…”

Section: Asymmetric Kernelsmentioning

confidence: 99%

Asymmetric kernel in Gaussian Processes for learning target variance

Pintea

Gemert

Smeulders

2018

Pattern Recognition Letters

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This work incorporates the multi-modality of the data distribution into a Gaussian Process regression model. We approach the problem from a discriminative perspective by learning, jointly over the training data, the target space variance in the neighborhood of a certain sample through metric learning. We start by using data centers rather than all training samples. Subsequently, each center selects an individualized kernel metric. This enables each center to adjust the kernel space in its vicinity in correspondence with the topology of the targets -a multi-modal approach. We additionally add descriptiveness by allowing each center to learn a precision matrix. We demonstrate empirically the reliability of the model.

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“…Many different kinds of priors have been incorporated into reconstruction-based methods, e.g. edge priors [43], gradient priors [42], steering kernel regression (SKR) [56,28], non-local means (NLMs) [4,29,22] and total variation (TV) [30]. Alternative ideas about prior knowledge and reconstruction constraints use Markov Random Fields (MRF) to impose probabilistic constraints on pixel consistency [3,17], and have been extended to combine these consistency constraints with predicted or expected image content [38,39,40].…”

Section: Accepted Manuscriptmentioning

confidence: 99%