Asymmetric, Non-unimodal Kernel Regression for Image Processing

Publisher:
IEEE Computer Society
Publication Type:
Conference Proceeding
Citation:
Proceedings. 2010 Digital Image Computing: Techniques and Applications (DICTA 2010), 2010, pp. 141 - 145
Issue Date:
2010-01
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Kernel regression has been previously proposed as a robust estimator for a wide range of image processing tasks, including image denoising, interpolation and superresolution. In this article we propose a kernel formulation that relaxes the usual symmetric and unimodal properties to effectively exploit the smoothness characteristics of natural images. The proposed method extends the kernel support along similar image characteristics to further increase the robustness of the estimates. Application of the proposed method to image denoising yields significant improvement over the previously reported regression methods and produces results comparable to the state-ofthe-art denoising techniques.
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