Affiliation:
1. Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China
2. Department of Digital Media Technology, Central China Normal University, Wuhan 430079, China
Abstract
Deep Unfolding Networks (DUNs) serve as a predominant approach for Compressed Sensing (CS) reconstruction algorithms by harnessing optimization. However, a notable constraint within the DUN framework is the restriction to single-channel inputs and outputs at each stage during gradient descent computations. This constraint compels the feature maps of the proximal mapping module to undergo multi-channel to single-channel dimensionality reduction, resulting in limited feature characterization capabilities. Furthermore, most prevalent reconstruction networks rely on single-scale structures, neglecting the extraction of features from different scales, thereby impeding the overall reconstruction network’s performance. To address these limitations, this paper introduces a novel CS reconstruction network termed the Multi-channel and Multi-scale Unfolding Network (MMU-Net). MMU-Net embraces a multi-channel approach, featuring the incorporation of Adap-SKConv with an attention mechanism to facilitate the exchange of information between gradient terms and enhance the feature map’s characterization capacity. Moreover, a Multi-scale Block is introduced to extract multi-scale features, bolstering the network’s ability to characterize and reconstruct the images. Our study extensively evaluates MMU-Net’s performance across multiple benchmark datasets, including Urban100, Set11, BSD68, and the UC Merced Land Use Dataset, encompassing both natural and remote sensing images. The results of our study underscore the superior performance of MMU-Net in comparison to existing state-of-the-art CS methods.
Subject
General Physics and Astronomy
Reference52 articles.
1. Compressed sensing;Donoho;IEEE Trans. Inf. Theory,2006
2. An Introduction to Compressive Sampling;Candes;IEEE Signal Process. Mag.,2008
3. Single-Pixel Imaging via Compressive Sampling;Duarte;IEEE Signal Process. Mag.,2008
4. Adaptive Basis Scan by Wavelet Prediction for Single-Pixel Imaging;Rousset;IEEE Trans. Comput. Imaging,2016
5. Wu, Z., Zhang, Z., Song, J., and Zhang, M. (2021, January 5–9). Spatial-temporal synergic prior driven unfolding network for snapshot compressive imaging. Proceedings of the 2021 IEEE International Conference on Multimedia and Expo (ICME), Shenzhen, China.