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 decoder. The sensing and decoder networks have a significant impact on the reconstruction performance, and it is obvious that both two networks must be in harmony. However, previous studies have focused on designing the loss function considering only the decoder network. In this paper, we propose a novel training process that can learn sensing and decoder networks simultaneously using Information Bottleneck (IB) theory. By maximizing importance through proposed importance generator, the sensing network is trained to compress important information for image reconstruction of the decoder network. The representative experimental results demonstrate that the proposed method is applied in recently proposed CS algorithms and increases the reconstruction performance with large margin in all CS ratios.

中文翻译：

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压缩感知图像重建的信息瓶颈测量

由于深度网络的进步，图像压缩感知（CS）取得了很大的性能提升。CS方法一般由传感和解码器组成。感知和解码器网络对重建性能有显着影响，很明显这两个网络必须协调一致。然而，以前的研究集中在仅考虑解码器网络的损失函数设计上。在本文中，我们提出了一种新颖的训练过程，可以使用信息瓶颈 (IB) 理论同时学习感知和解码器网络。通过提出的重要性生成器最大化重要性，训练感知网络以压缩重要信息以用于解码器网络的图像重建。