Multi-Scale Deep Compressive Sensing Network
This addresses image quality degradation in compressive sensing applications, though it appears incremental as it builds on existing deep learning-based compressive sensing methods.
The paper tackles the problem of high-frequency content loss in deep learning-based compressive sensing image reconstruction at low sampling rates by proposing a multi-scale DCS convolutional neural network (MS-DCSNet) that uses wavelet transforms and multi-scale convolution, resulting in better reconstruction quality.
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.