Optics, Volume 5, Issue 4 (December 2024) – 5 articles

The World Health Organization (WHO) cancer agency predicts that more than 35 million cases of cancer will be experienced in 2050, a 77% increase over the 2022 estimate. Currently, the main cancers diagnosed are breast, lung, and colorectal. There is no standardized tool for cancer diagnoses; initially, clinical procedures are guided by the patient symptoms and usually involve biochemical blood tests, imaging, and biopsy. Label-free non-linear optical approaches are promising tools for tumor imaging, due to their inherent non-invasive biosafe contrast mechanisms and the ability to monitor collagen-related disorders, and biochemical and metabolic changes during cancer progression. In this review, the main non-linear microscopy techniques are discussed, according to three main contrast mechanisms: biochemical, metabolic, and structural imaging. Full article

In order to break the diffraction limit and improve the imaging resolution of optical microscope, in this article, we theoretically deduced the influence of phase difference on imaging resolution under coherent illumination. As the phase difference increased, the resolution improved gradually. Inspired by this conclusion, a super-resolution optical imaging system based on phase modulation was proposed and simulated. An optical mask was designed to generate additional phase difference for the adjacent area at the sample’s surface, and the influence of its structural parameters was analyzed numerically. The simulation results preliminarily confirm the feasibility of this scheme, laying the foundation for a more optimal and comprehensive super-resolution imaging scheme. Due to its advantages of high resolution, a wide field of view, and being compatible, this non-fluorescence super-resolution imaging scheme is worthy of further research and application. Full article

To address the challenge of image matching posed by significant modal differences in remote sensing images influenced by snow cover, this paper proposes an innovative image transformation-based matching method. Initially, the Pix2Pix-GAN conversion network is employed to transform remote sensing images with snow cover into images without snow cover, reducing the feature disparity between the images. This conversion facilitates the extraction of more discernible features for matching by transforming the problem from snow-covered to snow-free images. Subsequently, a multi-level feature extraction network is utilized to extract multi-level feature descriptors from the transformed images. Keypoints are derived from these descriptors, enabling effective feature matching. Finally, the matching results are mapped back onto the original snow-covered remote sensing images. The proposed method was compared to well-established techniques such as SIFT, RIFT2, R2D2, and ReDFeat and demonstrated outstanding performance. In terms of NCM, MP, Rep, Recall, and F1-measure, our method outperformed the state of the art by 177, 0.29, 0.22, 0.21, and 0.25, respectively. In addition, the algorithm shows robustness over a range of image rotation angles from −40° to 40°. This innovative approach offers a new perspective on the task of matching multi-temporal snow-covered remote sensing images. Full article

In order to test the performance of laser devices for attacking miniature UAVs, we studied the principle of laser devices on soft killing and hard killing. Then, the flight test conditions of miniature UAVs were constructed, and the laser devices were tested and evaluated with the two indexes of maximum jamming range and maximum intercepting range. The first step involves calculating the far-field beam power density corresponding to the unmanned aerial vehicle (UAV) detection equipment and laser device at different distances. Subsequently, the signal electron count received by the UAV detector from the incident laser source target within the integration time tint is computed and compared against the full well charge of the photodetector. This comparison analyzes the UAV detector’s potential for dazzle/blind effects. When the laser device is positioned 600 m from the UAV, the ratio of signal electrons received by the detector to the full well charge was 13.53, indicating that the detector receives signal electrons exceeding the full well charge by over 10 times, thus causing UAV detector blindness. At a distance of 1.2 km from the UAV, this ratio reduces to 2.92, where the detector receives signal electrons around three times the full well charge, causing UAV detector dazzle. Experimental testing determines that the maximum interception distance of this laser device for small, slow-moving UAV equipment is 500 m. Finally, it is proved that the method can effectively test the attacking performance of laser devices, and provides a basis for improving the function and performance of laser devices. Full article

Surface-enhanced Raman scattering (SERS) holds significant potential across environmental monitoring, materials science, and biomedical applications. However, challenges regarding ultra-sensitive detection and repeatability are bottlenecks for practical applications, especially in terms of detection uniformity. In this study, we utilized surface acoustic waves (SAW) in conjunction with Raman spectroscopy to actively enrich 5 μL of 50 nm gold nanoparticles (AuNPs), thereby achieving innovative SERS-active sensing. This dynamic enrichment process enables the dense and uniform aggregation of AuNPs in droplets, thereby facilitating reliable ultrasensitive detection. The SAW system was further optimized through hydrophobic surface treatment. Using 4-mercaptobenzoic acid as a probe analyte, our SAW-SERS method successfully detected concentrations as low as 10⁻⁸ mol/L. The surface acoustic waves had the capability to significantly amplify Raman signal intensity up to 100 compared to conventional drying methodologies. This SAW-induced AuNP clustering technology offers a rapid, label-free SERS sensing method characterized by exceptional sensitivity and uniformity. Full article