# Serial Decoders-Based Auto-Encoders for Image Reconstruction  
[PERSON]  
1 School of Information Engineering, Yangzhou University, Yangzhou  
to investigate new algorithms that are based on deep learning. Currently, merely some of the components of traditional data compression methods such as prediction coding, transformation coding, and quantization coding are being replaced by deep learning methods. The principal difficulty is that lossless data reconstruction of pure deep learning-based methods has not yet been attained. In consideration of the powerful capabilities of deep learning, this article will explore new approaches to data reconstruction via pure deep-learning based methods. Auto-encoders (AE) are a classical architecture of deep neural networks, which initially project high-dimensional data into a low-dimensional latent space according to a given rule, and then reconstruct the original data from latent space while minimizing reconstruction error [3; 4; 5; 6]. Auto-encoders possess many theoretical models, including the following: sparse auto-encoders, convolutional auto-encoders, variational auto-encoders (VAE), adversarial auto-encoders (AAE), Wasserstein auto-encoders (WAE), graphical auto-encoders, extreme learning auto-encoders, integral learning auto-encoders, inverse function auto-encoders, recursive or recurrent auto-encoders, double or couple auto-encoders, de-noising auto-encoders, generative auto-encoders, fuzzy auto-encoders, non-negative auto-encoders, binary auto-encoders, quantum auto-encoders, linear auto-encoders, blind auto-encoders, group auto-encoders, kernel auto-encoders, etc. [3; 4; 5; 6]. Some of the theoretical frameworks of traditional auto-encoders are collected in Table 1. Auto-encoders have garnered extensive research and applications in the domains of classification, recognition, encoding, sensing, and processing [3; 4; 5; 6]. Since auto-encoders comprise encoding and decoding units, they hold the potential of being applied to high-performance data compression and signal-compressed sensing [7; 8]. Classical auto-encoders shall be referred to as narrow auto-encoders. Other deep learning-based methods of data compression and signal-compressed sensing shall be referred to as generalized auto-encoders, because they contain encoding and decoding components, and each component can introduce an auto-encoder unit [9; 10]. Narrow and generalized auto-encoders-based approaches of data compression and signal-compressed sensing can provide better performance in data reconstruction than the classical approaches [7; 8; 9; 10]. However, current research in auto-encoders exhibits the following problems: the research objective is not to achieve lossless data reconstruction but efficient feature representation; independent evaluation of the performance of data reconstruction is neglected; the performance of data reconstruction needs to be improved; it is difficult to attain lossless data reconstruction [17; 18]. For instance, the performance of data reconstruction using AAE, one of the most advanced auto-encoders, is shown in Figure 1[11]. The horizontal axis is the dimension of the latent space and the vertical axis is the average structural similarity (SSIM) of reconstructed images in comparison with original images. It is indicated in Figure 1 that the performance in data reconstruction of AAE increases while the dimension of hidden space increases, making it difficult to achieve lossless data reconstruction. Currently, pure deep learning-based methods of data compression and signal-compressed sensing cannot attain lossless data reconstruction. \begin{table}
\begin{tabular}{c c} \hline \hline
**Auto-Encoders** & **References** \\ \hline Variational auto-encoders & [4] \\ Adversarial auto-encoders & [11] \\ Convolutional auto-encoders & [12] \\ Quantum auto-encoders & [13] \\ Sparse auto-encoders & [14] \\ Wasserstein auto-encoders & [15] \\ Graphical auto-encoders & [16] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Some of the theoretical frameworks of traditional auto-encoders.