Laplacian Pyramid-like Autoencoder
This work addresses image processing tasks like classification and super-resolution, but it appears incremental by adapting an existing signal processing concept to autoencoders.
The paper tackles image analysis by introducing the Laplacian pyramid-like autoencoder (LPAE), which decomposes images into approximation and detail components, resulting in a lighter classification model with substantially high performance and improved super-resolution outcomes.
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.