  Convolutional Sparse Coding (CSC) is a well-established image representation
model especially suited for image restoration tasks. In this work, we extend
the applicability of this model by proposing a supervised approach to
convolutional sparse coding, which aims at learning discriminative dictionaries
instead of purely reconstructive ones. We incorporate a supervised
regularization term into the traditional unsupervised CSC objective to
encourage the final dictionary elements to be discriminative. Experimental
results show that using supervised convolutional learning results in two key
advantages. First, we learn more semantically relevant filters in the
dictionary and second, we achieve improved image reconstruction on unseen data.
