Multi-Channel Adaptive Partitioning Network for Block-Based Image Compressive Sensing

Image compressive sensing (CS) technology has attracted increasing attentions in the past few years, and a great deal deep learning-based methods have been proposed. However, the existing methods use fixed-scale blocks for sampling and re-construction. Such practice will inevitably result in the in-ability to distinguish between significant regions and background regions, and even waste excessive sampling resources on the background ones to a large extent. In this paper, we propose a novel multi-channel adaptive partitioning network for block-based image CS, in which image blocks of different scales are utilized to distinguish regions of different saliency. Specifically, an adaptive block partitioning method based on image saliency is put forward, using which significant regions are divided into large blocks and background regions are divided into small blocks. Subsequently, blocks of different scales are fed to different-channel networks for sampling to yield the compressed measurements. To improve the re-construction quality of the image, a scalable multi-scale re-construction network is proposed to recover the compressed measurements into the reconstructed image. Experimental results compared with the state-of-the-art show that the proposed scheme achieves significant improvements in terms of objective metrics and subjective visual image quality.