Search for: All records

It is generally assumed that oceanic effects, such as absorption, scattering, and turbulence, deteriorate underwater optical imaging and/or signal detection. In this paper, we present an interesting observation that slight turbidity may actually improve the performance of underwater optical imaging in the presence of occlusion. We have carried out simulations and optical experiments in underwater degraded environments to investigate this hypothesis. For simulation, the Monte Carlo method was used to analyze the influence of imaging performance under varying turbidity and occlusion conditions. Additionally, optical experiments were conducted under various turbid and partially occluded environments. We considered the effects of different parameters such as varying turbidity levels, severity of partial occlusion, number of photons, propagation distances, and imaging modality. The simulation results we performed suggest that, regardless of the variation of the imaging system and degradation parameters, slight turbidity may improve underwater imaging performance in occlusion. The optical experimental results are also in agreement with the simulation results that slightly increasing the turbidity levels may boost the image quality in the scenarios we considered. To the best of our knowledge, this is the first report to theoretically analyze and experimentally validate the phenomenon that turbidity may improve underwater imaging performance in certain degraded environments. 

We propose a diffuser-based lensless underwater optical signal detection system. The system consists of a lensless one-dimensional (1D) camera array equipped with random phase modulators for signal acquisition and one-dimensional integral imaging convolutional neural network (1DInImCNN) for signal classification. During the acquisition process, the encoded signal transmitted by a light-emitting diode passes through a turbid medium as well as partial occlusion. The 1D diffuser-based lensless camera array is used to capture the transmitted information. The captured pseudorandom patterns are then classified through the 1DInImCNN to output the desired signal. We compared our proposed underwater lensless optical signal detection system with an equivalent lens-based underwater optical signal detection system in terms of detection performance and computational cost. The results show that the former outperforms the latter. Moreover, we use dimensionality reduction on the lensless pattern and study their theoretical computational costs and detection performance. The results show that the detection performance of lensless systems does not suffer appreciably. This makes lensless systems a great candidate for low-cost compressive underwater optical imaging and signal detection.