College of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun 130022, China.
School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway 47500, Malaysia.
Sensors (Basel). 2021 Jan 5;21(1):313. doi: 10.3390/s21010313.
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.
由于介质散射、吸收和复杂的光相互作用，从水下环境中捕捉物体一直是一项艰巨的任务。单像素成像 (SPI) 是一种有效的成像方法，可在低光照条件下获取空间物体信息。在本文中，我们提出了一种基于压缩感知超分辨率卷积神经网络 (CS-SRCNN) 的水下环境单像素目标检测系统。使用 CS-SRCNN 算法，图像重建可以使用图像总像素的 30%来实现。我们还研究了压缩比对水下物体 SPI 重建性能的影响。此外，我们分析了峰值信噪比 (PSNR) 和结构相似性指数 (SSIM) 的影响，以确定重建图像的图像质量。我们的工作与 SPI 系统和 SRCNN 方法进行了比较，以证明其在从水下环境中捕获目标结果方面的效率。所提出方法的 PSNR 和 SSIM 分别提高到 35.44%和 73.07%。这项工作为 SPI 应用提供了新的见解，并为水下光学目标成像创造了更好的选择，以实现高质量。
