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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.
This presentation is part of Minisymposium “MS40 - Recent Advances in Convolutional Sparse Representations”
organized by: Giacomo Boracchi (Politecnico di Milano) , Alessandro Foi (Tampere University of Technology) , Brendt Wohlberg (Los Alamos National Laboratory) .