Sparse coding has been proposed as a guiding principle in neural representations of sensory input, particularly in the visual system. Because sparse codes are defined as representations with low activity ratios—i.e., at any given time a small proportion of neurons are active—they are sometimes proposed as a means to help conserve metabolic costs. Although we accept that such metabolic costs pla...

Convolutional sparse coding improves on the standard approximation by incorporating a global shift-invariant model. The most efficient convolutional methods are based alternating direction method of multipliers and convolution theorem. only major difference between these is how they approach least-squares fitting subproblem. This letter presents solution to this subproblem, which efficiency sta...