Image Compressed Sensing (CS) has achieved a lot of performance improvement thanks to
advances in deep networks. The CS method is generally composed of a sensing and a …

Current facial expression recognition systems de-mand an expensive re-training routine
when deployed to different scenarios than they were trained for. Biasing them towards …

Reconstructing natural image from its corresponding compressive sensing (CS)
measurements is an ill-posed problem. Learning accurate prior of desirable image is …

The existing freight train detection model could not meet the demand of actual applications.
Aiming at the problem of typical train image fault detection of freight trains, a multi-class …

This paper presents a two-stream learning-based compressive sensing network with a high-
frequency compensation module (TSLCSNet) that betters restores the detailed components …

Despite the tremendous success of convolutional neural networks (CNNs) in computer
vision, the mechanism of CNNs still lacks clear interpretation. Currently, class activation …

We propose a simple and efficient framework for video compressive sensing (CS) at fixed
scene. In recent years, deep learning has been widely used in reconstructing images and …

Recent work showed neural-network based approaches to reconstructing images from
compressively sensed measurements offer significant improvements in accuracy and signal …

In order to deal with the high dimensional and high complex process data, a residual
structure based dual extreme learning machine model (R-DELM) is proposed. This method …