Abstract
With joint learning of sampling and recovery, the deep learning-based compressive sensing (DCS) has shown significant improvement in performance and running time reduction. Its reconstructed image, however, losses high-frequency content especially at low subrates. This happens similarly in the multi-scale sampling scheme which also samples more low-frequency components. In this paper, we propose a multi-scale DCS convolutional neural network (MS-DCSNet) in which we convert image signal using multiple scale-based wavelet transform, then capture it through convolution block by block across scales. The initial reconstructed image is directly recovered from multi-scale measurements. Multi-scale wavelet convolution is utilized to enhance the final reconstruction quality. The network is able to learn both multi-scale sampling and multi-scale reconstruction, thus results in better reconstruction quality.
Abstract (translated)
通过对采样和恢复的联合学习,基于深度学习的压缩感知(DCS)已显示出性能和运行时间缩短的显着改进。然而,其重建的图像会丢失高频内容,尤其是在低子速率下。这在多尺度采样方案中类似地发生,其也采样更多的低频分量。在本文中,我们提出了一种多尺度DCS卷积神经网络(MS-DCSNet),其中我们使用基于多尺度的小波变换来转换图像信号,然后通过跨越尺度的块卷积来捕获它。初始重建图像直接从多尺度测量中恢复。利用多尺度小波卷积来提高最终的重建质量。该网络能够学习多尺度采样和多尺度重建,从而产生更好的重建质量。
URL
https://arxiv.org/abs/1809.05717