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A structure-adaptive vector median filter (SAVMF) for removal of impulse noise from color images is presented in this paper. A color image is represented in quaternion form, and then quaternion Fourier transform is employed to detect the dominant orientation of the pattern in a local neighborhood. Based on the local orientation and its strength, the size, shape and orientation of the support window of vector median filter (VMF) can be adaptively computed, and thus structure-adaptive VMF is implemented.

We propose an efficient method for image registration based on iteratively fitting a parametric model to the output of an elastic registration. It combines the flexibility of elastic registration - able to estimate complex deformations - with the robustness of parametric registration - able to estimate very large displacement. Our approach is made feasible by using the recent Local All-Pass (LAP) algorithm; a fast and accurate filter-based method for estimating the local deformation between two images. Moreover, at each iteration we

With the prevalence of digital products like cellphone, tablet and personal computer, the screen content image (SCI) consisting of text, graphic, and natural scene picture becomes a significant media in various communication scenarios. Consequently, we proposed a reduced-reference quality metric dedicated for SCI.

Blur estimation is critical to blind image deconvolution. In this work, by taking Gaussian kernel as an example, we propose an approach to estimate the blur size for photon-limited images. This estimation is based on the minimization of a novel criterion, blur-PURE (Poisson unbiased risk estimate), which makes use of the Poisson noise statistics of the measurement. Experimental results demonstrate the effectiveness of the proposed method in various scenarios.

Occlusion is one of the most challenging factors in visual tracking. In this paper, we propose a novel context-based occlusion detection algorithm for robust visual tracking. The basic idea of our algorithm is that occlusion indicates that

Stereoscopic reconstruction is important to automatic vision systems. As an intermediate step, estimating this reconstruction is not enough for good performance of the whole system, and its uncertainty must be characterized. Several methods propose uncertainty indexes based on specific data features, thus incomplete, while others are based on learning. We propose a simple index, named ambiguity index, taking into account both data and regularization, and derived directly from the optimization process.

Several algorithms in image processing involve spatio-range filtering of images and high-dimensional data derived from them. The exact computation of these so-called high-dimensional filters is challenging, especially for real-time processing of high-resolution images. In this paper, a simple yet accurate approximation of high-dimensional filters is obtained using a mix of clustering and fast convolutions. The resulting algorithm is competitive with state-of-the-art methods and importantly comes with guaranteed error bounds. The algorithm will be discussed in short in this presentation.

Retinex theory deals with compensation for illumination effects in images, which is usually an ill-posed problem. The existence of noises may severely challenge the performance of Retinex algorithms. Therefore, the main aim of this paper is to present a general variational Retinex model to effectively and robustly restore images corrupted by both noises and intensity inhomogeneities. Our strategy is to simultaneously recover the noise-free image and decompose it into reflectance and illumination component.