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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.
Mengdi Li; Anumol Mathai; Stephen Lau; Jian Yam; Xiping Xu; Xin Wang. Underwater Object Detection and Reconstruction Based on Active Single-Pixel Imaging and Super-Resolution Convolutional Neural Network. Sensors 2021, 21, 313 .
AMA StyleMengdi Li, Anumol Mathai, Stephen Lau, Jian Yam, Xiping Xu, Xin Wang. Underwater Object Detection and Reconstruction Based on Active Single-Pixel Imaging and Super-Resolution Convolutional Neural Network. Sensors. 2021; 21 (1):313.
Chicago/Turabian StyleMengdi Li; Anumol Mathai; Stephen Lau; Jian Yam; Xiping Xu; Xin Wang. 2021. "Underwater Object Detection and Reconstruction Based on Active Single-Pixel Imaging and Super-Resolution Convolutional Neural Network." Sensors 21, no. 1: 313.
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.
Anumol Mathai; Ningqun Guo; Dong Liu; Xin Wang. 3D Transparent Object Detection and Reconstruction Based on Passive Mode Single-Pixel Imaging. Sensors 2020, 20, 4211 .
AMA StyleAnumol Mathai, Ningqun Guo, Dong Liu, Xin Wang. 3D Transparent Object Detection and Reconstruction Based on Passive Mode Single-Pixel Imaging. Sensors. 2020; 20 (15):4211.
Chicago/Turabian StyleAnumol Mathai; Ningqun Guo; Dong Liu; Xin Wang. 2020. "3D Transparent Object Detection and Reconstruction Based on Passive Mode Single-Pixel Imaging." Sensors 20, no. 15: 4211.
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.
Qi Chen; Anumol Mathai; Xiping Xu; Xin Wang. A Study into the Effects of Factors Influencing an Underwater, Single-Pixel Imaging System’s Performance. Photonics 2019, 6, 123 .
AMA StyleQi Chen, Anumol Mathai, Xiping Xu, Xin Wang. A Study into the Effects of Factors Influencing an Underwater, Single-Pixel Imaging System’s Performance. Photonics. 2019; 6 (4):123.
Chicago/Turabian StyleQi Chen; Anumol Mathai; Xiping Xu; Xin Wang. 2019. "A Study into the Effects of Factors Influencing an Underwater, Single-Pixel Imaging System’s Performance." Photonics 6, no. 4: 123.