Low-level structure feature extraction for image processing via stacked sparse denoising autoencoder

In this paper, we propose a novel low-level structure feature extraction for image processing based on deep neural network, stacked sparse denoising autoencoder (SSDA). The current image processing methods via deep learning are directly building and learning the end-to-end mappings between the input/output. Instead, we advocate the analysis of the first layer learning features from input data. With the learned low-level structure features, we improve two edge-preserving filters that are key to image processing tasks such as denoising, High Dynamic Range (HDR) compression and details enhancement. Due to the validity and superiority of the proposed feature extraction, the results computed by the two improved filters do not suffer from the drawbacks including halos, edge blurring, noise amplification and over-enhancement. More importantly, we demonstrate that the features trained from natural images are not specific and can extract the structure features of infrared images. Hence, it is feasible to handle tasks by using the trained features directly.