Learned Filters

Learned filters

These are examples of 16x16 sparsifying filters, as described in [1]. They were learned on a set of five images and are constrained to form a frame for the space of N by N images. While the filters appear similar to Gabor atoms or the filters learned with a convolutional neural network, they were learned to satisfy a significantly different optimality condition. A preprint of the full paper will be available soon.

  1. L. Pfister and Y. Bresler, “Learning Sparsifying Filter Banks,” in Proc. SPIE Wavelets & Sparsity XVI, 2015, vol. 9597.
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