  Autoencoders are composed of coding and decoding units, hence they hold the
inherent potential of high-performance data compression and signal compressed
sensing. The main disadvantages of current autoencoders comprise the following
several aspects: the research objective is not data reconstruction but feature
representation; the performance evaluation of data recovery is neglected; it is
hard to achieve lossless data reconstruction by pure autoencoders, even by pure
deep learning. This paper aims for image reconstruction of autoencoders,
employs cascade decoders-based autoencoders, perfects the performance of image
reconstruction, approaches gradually lossless image recovery, and provides
solid theory and application basis for autoencoders-based image compression and
compressed sensing. The proposed serial decoders-based autoencoders include the
architectures of multi-level decoders and the related optimization algorithms.
The cascade decoders consist of general decoders, residual decoders,
adversarial decoders and their combinations. It is evaluated by the
experimental results that the proposed autoencoders outperform the classical
autoencoders in the performance of image reconstruction.
