Enhancing Single Image Compressive Sensing Via Pyramid-Based Multi-Scale Sampling

Recently, single image compressive sensing has advanced considerably and achieved favorable performance. However, most methods only focus on designing effective reconstruction networks yet ignoring crude sampling strategy, where less effective information in measurement values makes it impossible to reconstruct high-quality images, especially at low sampling rates. To break aforementioned obstacle, we propose a novel pyramid sampling-based compressive sensing network, termed PCSNet, for single image compressive sensing reconstruction. Specifically, the novelties of our proposed PCSNet are mainly two-fold. Firstly, we design a novel pyramid compressive sampling (PCS) scheme to obtain measurement values from image pyramids, which ensures that measurements carry more context and multi-scale information, conducive to precise CS reconstruction. Moreover, a scale assignment module (SAM) is proposed to dynamically stress on the significance of different scales. Secondly, we propose a pyramid reconstruction network (PRN) to be suitable for PCS, which parallelly reconstruct compressive images from multi-scale measurements. An information aggregation module (IAM) is proposed in PRN to mutually supply structural and detail information of different scales. Extensive experimental results on three datasets manifest our proposed PCSNet outperforms other state-of-the-art CS models. The source code can be downloaded via https://github.com/WHK-Huake/PCSNet.