Laplacian Pyramid-like Autoencoder

TL;DR

The paper introduces the Laplacian pyramid-like autoencoder (LPAE), which decomposes images into approximation and detail components to improve classification efficiency and super-resolution quality, combining image analysis with autoencoder design.

Contribution

LPAE integrates Laplacian pyramid concepts into autoencoders, enabling lighter classification models and enhanced super-resolution reconstruction.

Findings

LPAE produces lighter classification models without sacrificing accuracy.

The decoder achieves high-quality image reconstruction in super-resolution tasks.

LPAE outperforms traditional autoencoders in image detail preservation.

Abstract

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.