Due to medium scattering, absorption, and complex light interactions, capturing objects from the underwater environment has always been a difficult task. Single-pixel imaging (SPI) is an efficient imaging approach that can obtain spatial object information under low-light conditions. In this paper, we propose a single-pixel object inspection system for the underwater environment based on compressive sensing super-resolution convolutional neural network (CS-SRCNN). With the CS-SRCNN algorithm, image reconstruction can be achieved with 30% of the total pixels in the image. We also investigate the impact of compression ratios on underwater object SPI reconstruction performance. In addition, we analyzed the effect of peak signal to noise ratio (PSNR) and structural similarity index (SSIM) to determine the image quality of the reconstructed image. Our work is compared to the SPI system and SRCNN method to demonstrate its efficiency in capturing object results from an underwater environment. The PSNR and SSIM of the proposed method have increased to 35.44% and 73.07%, respectively. This work provides new insight into SPI applications and creates a better alternative for underwater optical object imaging to achieve good quality.
Transparent object detection and reconstruction are significant, due to their practical applications. The appearance and characteristics of light in these objects make reconstruction methods tailored for Lambertian surfaces fail disgracefully. In this paper, we introduce a fixed multi-viewpoint approach to ascertain the shape of transparent objects, thereby avoiding the rotation or movement of the object during imaging. In addition, a simple and cost-effective experimental setup is presented, which employs two single-pixel detectors and a digital micromirror device, for imaging transparent objects by projecting binary patterns. In the system setup, a dark framework is implemented around the object, to create shades at the boundaries of the object. By triangulating the light path from the object, the surface shape is recovered, neither considering the reflections nor the number of refractions. It can, therefore, handle transparent objects with a relatively complex shape with the unknown refractive index. The implementation of compressive sensing in this technique further simplifies the acquisition process, by reducing the number of measurements. The experimental results show that 2D images obtained from the single-pixel detectors are better in quality with a resolution of 32×32. Additionally, the obtained disparity and error map indicate the feasibility and accuracy of the proposed method. This work provides a new insight into 3D transparent object detection and reconstruction, based on single-pixel imaging at an affordable cost, with the implementation of a few numbers of detectors.
Underwater detection has always been a challenge due to the limitations caused by scattering and absorption in the underwater environment. Because of their great penetration abilities, lasers have become the most suitable technology for underwater detection. In all underwater laser applications, the reflected laser pulse which contains the key information for most of the system is highly degraded along the laser’s propagation path and during reflection. This has a direct impact on the system’s performance, especially for single-pixel imaging (SPI) which is very dependent on light-intensity information. Due to the complications in the underwater environment, it is necessary to study the influential factors and their impacts on underwater SPI. In this study, we investigated the influence of the angle of incidence, target distance, and medium attenuation. A systematic investigation of the influential factors on the reflectance and ranging accuracy was performed theoretically and experimentally. The theoretical analysis was demonstrated based on the bidirectional reflection distribution function (BRDF) and laser detection and ranging (LADAR) model. Moreover, 2D single-pixel imaging (SPI) systems were setup for experimental investigation. The experimental results agree well with the theoretical results, which show the system’s dependency on the reflection intensity caused by the angle of incidence, target distance, and medium attenuation. The findings should be a reference for works looking to improve the performance of an underwater SPI system.
Modern digital cameras use sensor arrays that correspond to millions of pixels in the image. However, the single-pixel imaging (SPI) system captures images by a single-pixel detector without the need for a pixelated sensor to have spatial resolution. Research on SPI has attracted more and more attention from scholars. The SPI system provides a potential low-cost solution for sensing beyond the visible spectrum. Also, it is suitable for obtaining information in low light, high absorption, and backscattering conditions. This paper reviews the developments and performance of SPI, the research on static and dynamic objects, the methods of the real-time three-dimensional imaging and video. The potential applications of SPI are further explained in detail.
The detection and reconstruction of transparent objects have remained challenging due to the absence of their features and variations in the local features with variations in illumination. In this paper, both compressive sensing (CS) and super-resolution convolutional neural network (SRCNN) techniques are combined to capture transparent objects. With the proposed method, the transparent object’s details are extracted accurately using a single pixel detector during the surface reconstruction. The resultant images obtained from the experimental setup are low in quality due to speckles and deformations on the object. However, the implemented SRCNN algorithm has obviated the mentioned drawbacks and reconstructed images visually plausibly. The developed algorithm locates the deformities in the resultant images and improves the image quality. Additionally, the inclusion of compressive sensing minimizes the measurements required for reconstruction, thereby reducing image post-processing and hardware requirements during network training. The result obtained indicates that the visual quality of the reconstructed images has increased from a structural similarity index (SSIM) value of 0.2 to 0.53. In this work, we demonstrate the efficiency of the proposed method in imaging and reconstructing transparent objects with the application of a compressive single pixel imaging technique and improving the image quality to a satisfactory level using the SRCNN algorithm.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.