Eva Rifkah

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Image denoising is challenging due to the difficulty to differentiate noise from image fine details. Convolution with a Gaussian mask is a widely used method for denoising. In this paper we propose, based on the relation between linear diffusion and Gaussian scale space, estimators of both the variance and window size of the discrete Gaussian filter applied(More)
Anisotropic diffusion (ATD) is an edge-oriented, scale-space based, and iterative image-smoothing process. Two main challenges of ATD are how to automatically stop the iterative process, so to avoid blurring, and how to determine the scale (or edge-strength) parameter, so to best differentiate between edge and noise. In this paper, we propose 1) an(More)
Non-linear diffusion (ND) is an iterative difference equation used in several image processing applications such as denoising, segmentation, or compression. The number of iterations required to achieve optimal processing can be very high, making ND not suitable for real-time requirements. In this paper, we study how to reduce complexity of ND so as to(More)
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