  Convolutional sparse coding (CSC) is an important building block of many
computer vision applications ranging from image and video compression to deep
learning. We present two contributions to the state of the art in CSC. First,
we significantly speed up the computation by proposing a new optimization
framework that tackles the problem in the dual domain. Second, we extend the
original formulation to higher dimensions in order to process a wider range of
inputs, such as RGB images and videos. Our results show up to 20 times speedup
compared to current state-of-the-art CSC solvers.
