Research on engineering “hotspots” in the field of surface-enhanced Raman scattering (SERS) is at the forefront of contributing to the best sensing indicators. Currently, there is still an urgent need to design a high-strength and large-scale electric field distribution method in order to obtain an ideal SERS sensor. Here, we designed a three-dimensional (3D) Au/Ag nanoparticle (NP)/crossed carbon nanotube film SERS substrate. The proposed structure formed by the simple preparation process can perfectly coordinate the interaction between the SERS substrates, lasers, and molecules. The denser “hotspots” can be induced and then distributed in holes enclosed by Au/AgNPs and the gaps between them. This process was verified by numerical simulations. The experimental results show that the proposed SERS substrate possesses an excellent sensitivity of 10⁻¹² M (rhodamine 6G (R6G)), an enhancement factor of 1.60 × 10⁹, and a good signal reproducibility (the relative standard deviation is ~6.03%). We further use a Au/AgNP/crossed CNT substrate to detect complex solutions composed of toxic molecules, which shows that our proposed SERS substrate has a wide range of application potentials, especially in food safety.

Speckle is an unavoidable noise-like phenomenon in Synthetic Aperture Radar (SAR) imaging. In order to remove speckle, many despeckling methods have been proposed during the past three decades, including spatial-based methods, transform domain-based methods, and non-local filtering methods. However, SAR images usually contain many different types of regions, including homogeneous and heterogeneous regions. Some filters could despeckle effectively in homogeneous regions but could not preserve structures in heterogeneous regions. Some filters preserve structures well but do not suppress speckle effectively. Following this theory, we design a combination of two state-of-the-art despeckling tools that can overcome their respective shortcomings. In order to select the best filter output for each area in the image, the clustering and Gray Level Co-Occurrence Matrices (GLCM) are used for image classification and weighting, respectively. Clustering and GLCM use the co-registered optical images of SAR images because their structure information is consistent, and the optical images are much cleaner than SAR images. The experimental results on synthetic and real-world SAR images show that our proposed method can provide a better objective performance index under a strong noise level. Subjective visual inspection demonstrates that the proposed method has great potential in preserving structural details and suppressing speckle noise.