earticle

영어

In recent years, l1 norm is usually considered as the regularization term in the field of sparse representation. However, the non-zero entries obtained by the l1 regularization term always neglect the correlations with each other. In fact, different relationships or structures among non-zero entries are necessary in many applications. K-support norm is firstly proposed in the field of sparse prediction. The most important property of the k-support norm is grouping feature of the largest entries in the obtained solution. In this paper, we present a new image processing model by introducing the k-support norm to image gradient domain. The proposed model can be applied to image denoising and edge detection simultaneously. Some examples demonstrate the effectiveness of the novel model and its improvements.