The Convolutional Sparse Coding Model has drawn much intention in the past decade, due to its relevance in handling image processing tasks, and due to its connection to Convolutional Neural Networks (CNN). Two central questions that this model pose are (i) Pursuit: Given the model filters and an input image, find the appropriate sparse vector to represent the image effectively; and (ii) Learning: Given a set of images, find the filters that best represent this corpus. Both these questions have been the topics of many papers, offering various algorithms and experiments.
Our recent paper (see below) offers a very appealing answer to both the pursuit and the learning, by operating locally on the incoming images, and using a simple yet effective optimization strategy – coordinate descent. Our algorithms can operate on-line (even on one image), their performance is very competitive and often times state-of-the-art, and their code is simple to follow and deploy. The accompanying software package reproduces the results in the above-mentioned paper, along with a demonstration of these algorithms on two image processing applications.