Light carries an extraordinary amount of information, far beyond that is typically understood as a two-dimensional image. In fact, light fields are characterized by a much larger dimensionality, encoding information about spectrum, polarization, and orientation. Extracting this information can provide critical insights in diverse settings, ranging from space observation to medical imaging. Despite the benefits, the extraction process is very challenging, requiring close synergies between sensor designs, signal processing algorithms and knowledge extracting mechanisms. Driven by this motivation, SPL research activities on Imaging aspire to exploit, and extend theoretical results on Machine Learning, Compressed Sensing, and Sparse Representations to emerging applications, ranging from Active Range Imaging, to Land-cover classification, and Computational Photography.