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The sparsity principle, which consists of representing some phenomena with as few variables as possible, has been recently exploited with success in variational image processing. Most of the activities in this context have been dedicated to two (overlapping) research areas. The first includes works pursuing the design of new sparsity-promoting priors both in synthesis-based, analysis-based, and hybrid models. The second deals with devising efficient and robust algorithms for solving the typically large scale and possibly non-smooth non-convex optimization problems raised by sparsity-constrained regularization. This mini-symposium addresses theoretical and numerical issues which arise from designing sparsity-promoting techniques in variational image processing.