Laplacian Pyramid-like Autoencoder

Sangjun Han, Taeil Hur, Youngmi Hur

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish
Title of host publicationIntelligent Computing - Proceedings of the 2022 Computing Conference
EditorsKohei Arai
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages20
ISBN (Print)9783031104633
Publication statusPublished - 2022
EventComputing Conference, 2022 - Virtual, Online
Duration: 2022 Jul 142022 Jul 15

Publication series

NameLecture Notes in Networks and Systems
Volume507 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389


ConferenceComputing Conference, 2022
CityVirtual, Online

Bibliographical note

Funding Information:
Y. Hur—This work was supported in part by National Research Foundation of Korea (NRF) [Grant Numbers 2015R1A5A1009350 and 2021R1A2C1007598], and by the ‘Ministry of Science and ICT’ and NIPA via “HPC Support” Project.

Funding Information:
Acknowledgments. T. Hur—This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.2021-0-00023, Developing a lightweight Korean text detection and recognition technology for complex disaster situations).

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications


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