In this paper, we develop the Laplacian pyramid-like autoencoder (LPAE) by adding the Laplacian pyramid (LP) concept widely used to analyze images in Signal Processing. LPAE decomposes an image into the approximation image and the detail image in the encoder part and then tries to reconstruct the original image in the decoder part using the two components. We use LPAE for experiments on classifications and super-resolution areas. Using the detail image and the smaller-sized approximation image as inputs of a classification network, our LPAE makes the model lighter. Moreover, we show that the performance of the connected classification networks has remained substantially high. In a super-resolution area, we show that the decoder part gets a high-quality reconstruction image by setting to resemble the structure of LP. Consequently, LPAE improves the original results by combining the decoder part of the autoencoder and the super-resolution network.
Cite
@article{arxiv.2208.12484,
title = {Laplacian Pyramid-like Autoencoder},
author = {Sangjun Han and Taeil Hur and Youngmi Hur},
journal= {arXiv preprint arXiv:2208.12484},
year = {2022}
}
Comments
20 pages, 3 figures, 5 tables, Science and Information Conference 2022, Intelligent Computing