Learning adaptive filter banks for hierarchical image representation
P Yang, Y Shi, W Ding, X Sun… - 2014 IEEE Visual …, 2014 - ieeexplore.ieee.org
2014 IEEE Visual Communications and Image Processing Conference, 2014•ieeexplore.ieee.org
Conventional hierarchical image representation methods, eg 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 …
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 …
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.
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