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Despite the importance of distributed learning, few fully distributed support vector machines exist. In this paper, not only do we provide a fully distributed nonlinear SVM; we propose the first distributed constrained-form SVM. In the fully distributed context, a dataset is distributed among networked agents that cannot divulge their data, let alone centralize the data, and can only communicate with their neighbors in the network. Our strategy is based on two algorithms: the Douglas-Rachford algorithm and the projection-gradient method.

In this paper, we propose retinex-based perceptual contrast enhancement in images using luminance adaptation. We use the retinex theory to decompose an image into illumination and reflectance layers, and adopt luminance adaptation to handle the illumination layer which causes detail loss. First, we obtain the illumination layer using adaptive Gaussian filtering to remove halo artifacts. Then, we adaptively remove illumination of the illumination layer in the multi-scale retinex (MSR) process based on luminance adaptation to preserve details.

In this paper, we propose a new emission strategy in the context of plane wave imaging. Plane wave imaging indeed implies compounding in order to preserve a good image quality. Such compounding is usually obtained using multiple, successive emissions, which in turn yields a decrease of the frame rate. As opposed to this approach, our method is based on the simultaneous emission of several coded plane waves. This allows the reconstruction of all the images corresponding to the different plane waves, by using an inverse problem approach.