  We propose a convolutional recurrent sparse auto-encoder model. The model
consists of a sparse encoder, which is a convolutional extension of the learned
ISTA (LISTA) method, and a linear convolutional decoder. Our strategy offers a
simple method for learning a task-driven sparse convolutional dictionary (CD),
and producing an approximate convolutional sparse code (CSC) over the learned
dictionary. We trained the model to minimize reconstruction loss via gradient
decent with back-propagation and have achieved competitive results to KSVD
image denoising and to leading CSC methods in image inpainting requiring only a
small fraction of their run-time.
