An efficient deep convolutional laplacian pyramid architecture for CS reconstruction at low sampling ratios
This addresses image compression issues for applications requiring efficient reconstruction, but it is incremental as it builds on existing CS methods with architectural improvements.
The paper tackles the problem of blocking artifacts and ringing effects in compressed sensing image reconstruction at low sampling ratios by proposing a deep convolutional Laplacian Pyramid Compressed Sensing Network (LapCSNet), which achieves better performance in reconstructing details and sharper edges compared to state-of-the-art methods.
The compressed sensing (CS) has been successfully applied to image compression in the past few years as most image signals are sparse in a certain domain. Several CS reconstruction models have been proposed and obtained superior performance. However, these methods suffer from blocking artifacts or ringing effects at low sampling ratios in most cases. To address this problem, we propose a deep convolutional Laplacian Pyramid Compressed Sensing Network (LapCSNet) for CS, which consists of a sampling sub-network and a reconstruction sub-network. In the sampling sub-network, we utilize a convolutional layer to mimic the sampling operator. In contrast to the fixed sampling matrices used in traditional CS methods, the filters used in our convolutional layer are jointly optimized with the reconstruction sub-network. In the reconstruction sub-network, two branches are designed to reconstruct multi-scale residual images and muti-scale target images progressively using a Laplacian pyramid architecture. The proposed LapCSNet not only integrates multi-scale information to achieve better performance but also reduces computational cost dramatically. Experimental results on benchmark datasets demonstrate that the proposed method is capable of reconstructing more details and sharper edges against the state-of-the-arts methods.