Beyond Deep Residual Learning for Image Restoration: Persistent Homology-Guided Manifold Simplification
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2017)
Honolulu, Hawaii, USA
July 21, 2017 to July 26, 2017
The latest deep learning approaches perform better than the state-of-the-art signal processing approaches in various image restoration tasks. However, if an image contains many patterns and structures, the performance of these CNNs is still inferior. To address this issue, here we propose a novel feature space deep residual learning algorithm that outperforms the existing residual learning. The main idea is originated from the observation that the performance of a learning algorithm can be improved if the input and/or label manifolds can be made topologically simpler by an analytic mapping to a feature space. Our extensive numerical studies using denoising experiments and NTIRE single-image super-resolution (SISR) competition demonstrate that the proposed feature space residual learning outperforms the existing state-of-the-art approaches. Moreover, our algorithm was ranked high in the NTIRE competition with 5-10 times faster computational time compared to the top ranked teams.
Manifolds, Wavelet transforms, Noise reduction, Convolution, Image resolution, Complexity theory
W. Bae, J. Yoo and J. C. Ye, "Beyond Deep Residual Learning for Image Restoration: Persistent Homology-Guided Manifold Simplification," 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, Hawaii, USA, 2017, pp. 1141-1149.