Most of existing data-driven temperature imaging schemes for Tunable Diode Laser Absorption Spectroscopy (TDLAS) tomography are based on Convolutional Neural Network (CNN). However, some studies on CNN show that its actual perceptual field is much smaller than the theoretical one, which makes it not conducive for CNN to capture features from contextual information at long distance. In this work, a temperature imaging network based on Swin Transformer is established. To introduce cross-window connections while maintaining the efficient computation of local non-overlapped windows, Multi-headed Self-Attention (MSA) is computed alternatively in regularly partitioned windows and shifted windows. Simulation results show that the proposed network can reconstruct temperature images of higher quality than schemes based on CNN and Extreme Learning Machine (ELM) respectively.
1-bit compressed sensing (1-bit CS) examines the efficient acquisition of sparse signals via linear measurement systems followed by a 1-bit quantizer. In this paper, we discuss 1-bit CS reconstruction in the scenario that the sparsity level of the signal is unknown. We introduce reweighting approximate message passing (AMP) into the 1-bit CS problem and propose the binary iterative reweighting AMP algorithm (AMP-BRW). This algorithm performs binary reweighting AMP in the iterative process, which conforms to the binary manner of the 1-bit CS measurements and inherits the advantages of AMP. Simulation results show that AMP-BRW can realize 1-bit CS reconstruction without the prior knowledge of the sparse level of the signal. Moreover, AMP-BRW can achieve higher reconstruction performance and higher convergence performance than the original binary iterative reweighted algorithm.
Sparse representation matrix is of great significance for compressed sensing (CS). When dictionaries learned from training data are used instead of predefined dictionaries, signal reconstruction accuracy would be improved. In this paper, we learn dictionaries for compressed image reconstruction based on bilinear generalized approximate message passing (BiGAMP). Stochastic mapping is performed on the training data which are composed of image blocks, to conform to the statistical model of BiGAMP methodology. Square dictionary and overcomplete dictionary are learned respectively for blocked image sparse representation, and are applied to image CS reconstruction. Simulation results show that our learned dictionaries lead to improved image CS reconstruction performance in comparison to predefined dictionaries and dictionaries learned with K-SVD method.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.