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A second order Mumford-Shah model is proposed for image denoising. Unlike the original Mumford-Shah model, the proposed new model uses second order derivatives defined in bounded Hessian space as its regulariser. This model is capable of eliminating the undesirable staircase effect associated with the original Mumford-Shah model with a total variation(More)
The classical TV (Total Variation) model has been applied to gray texture image denoising and inpainting previously based on the non local operators, but such model can not be directly used to color texture image inpainting due to coupling of different image layers in color images. In order to solve the inpainting problem for color texture images(More)
—In this paper, we present a novel variational framework for multiphase synthetic aperture radar (SAR) image segmentation based on the fuzzy region competition method. A new energy functional is proposed to integrate the Gamma model and the edge detector based on the ratio of exponentially weighted averages (ROEWA) operator within the optimization process.(More)
Optical coherence tomography (OCT) is a three-dimensional non-invasive imaging technique that can generate images of the eye at microscopic level to help diagnosis of eye diseases. However, OCT images often suffer from inhomogeneity and are corrupted by speckle noise, posing challenges to automated OCT image segmentation and analysis. In this paper, a novel(More)
Image characteristics, such as texture, edge, smoothness, can be much better preserved by using nonlocal differential operators based on patch-distances in image processing. In this paper, we apply with nonlocal differential operators to some existing variation models of Retinex, such as the nonlocal variation model of Retinex (NL_VR); the nonlocal TV(More)