Abstract
The problem of long-distance imaging through time-varying scattering media, such as the atmosphere, is encountered in many science fields. Recent studies have demonstrated that random atmospheric variability can be considered a spatial light modulator in compressed sensing imaging. However, the quality of the reconstructed image needs to be further improved. In this paper, we propose a distributed cumulative synthesis method to improve the compressed sensing image reconstruction based on atmospheric modulation. For multiple original images of various types, the compressed sensing imaging simulation experiment with different sampling rates was conducted using the distributed cumulative synthesis method. The simulation results show that, compared with the imaging method using a single light source, the distributed cumulative synthesis method can effectively improve the quality of the reconstructed image, whether it is full sampling or undersampling. In addition, a sparsity impact factor is defined to quantify the reconstruction ability of the measurement matrix obtained by the distributed cumulative synthesis method. This value can be used as an evaluation index for the optimized design of the measurement matrix by the distributed cumulative synthesis method. Noise analysis shows that the proposed method has better anti-noise performance than the single light source imaging method.
Highlights
RANDOM scattering media can cause severe image quality degradation, rendering it difficult to image targets in or hidden behind a scattering medium [1,2,3]
In practice, it is difficult to assess whether a matrix satisfies the restricted isometry property (RIP) condition and to use the RIP to guide the design of the measurement matrix
This result shows that the distributed cumulative synthesis method has a better image reconstruction ability than the single light source method, which is consistent with the theoretical analysis
Summary
RANDOM scattering media can cause severe image quality degradation, rendering it difficult to image targets in or hidden behind a scattering medium [1,2,3]. Wavefront shaping [2,7,8,9] is a method that uses scattering media to achieve focusing and imaging at a desired position. This method requires auxiliary equipment in most cases, which limits its application in practice. With the development of deep learning [14], an increasing number of researchers are using neural networks to address these issues concerning the scattering media problem [15,16,17].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
