Rev-Ae: A Learned Frame Set For Image Reconstruction
2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING(2020)
摘要
Reversible residual network naturally extends the linear lifting scheme with no theoretic guarantee. In this paper, we propose a reversible autoencoder (Rev-AE) with this extended non-linear lifting scheme to improve image reconstruction. Nonlinear prediction and update operators are designed based on shallow convolutional neural networks to model multi-layer non-linearities. Different from existing autoencoders, Rev-AE support efficient image reconstruction with parameters reusable for the symmetric encoder and decoder. Rev-AE forms a set of related frames to guarantee perfect reconstruction with the non-linear extension of classic lifting scheme. Lower and upper bounds are developed for the set of frames to relate with the singular values for each non-linear operator. Furthermore, we employ Rev-AE into lossy image compression to evaluate its effectiveness on image reconstruction. Experimental results show that Rev-AE achieves competitive performance in comparison to the state-of-the-art.
更多查看译文
关键词
Lifting scheme, frame theory, image reconstruction, image compression
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要