Learning adaptive filter banks for hierarchical image representation

Conventional hierarchical image representation methods, e.g. Wavelet transform, use pre-determined filter banks which lack in adaption to the variant statistical characteristics of images. In this paper, we propose learning adaptive filter banks for hierarchical sparse image representation with a wavelet-like compact form using a deconvolutional network. The proposed scheme is verified by evaluating its sparsity in image representation. Experimental results demonstrate that the proposed scheme outperforms 9/7 and 5/3 wavelets transform in terms of both objective and subjective qualities under the same sparsity.