Global Sensing and Measurements Reuse for Image Compressed Sensing
This work addresses image compressed sensing for applications like medical imaging or video compression, but it is incremental as it builds on existing deep network-based methods.
The paper tackles the problem of image compressed sensing by proposing a network that collects all-level features and reuses measurements multiple times, achieving significant performance improvements over state-of-the-art methods on three benchmark datasets.
Recently, deep network-based image compressed sensing methods achieved high reconstruction quality and reduced computational overhead compared with traditional methods. However, existing methods obtain measurements only from partial features in the network and use them only once for image reconstruction. They ignore there are low, mid, and high-level features in the network\cite{zeiler2014visualizing} and all of them are essential for high-quality reconstruction. Moreover, using measurements only once may not be enough for extracting richer information from measurements. To address these issues, we propose a novel Measurements Reuse Convolutional Compressed Sensing Network (MR-CCSNet) which employs Global Sensing Module (GSM) to collect all level features for achieving an efficient sensing and Measurements Reuse Block (MRB) to reuse measurements multiple times on multi-scale. Finally, experimental results on three benchmark datasets show that our model can significantly outperform state-of-the-art methods.