In natural aquatic environments, the existence of colored dissolved organic matter (CDOM), suspended particles, and colloids can cause scattering and reflection of light and even emit fluorescence itself. Such interference negatively impacts algal fluorescence, further making it unreliable to measure the algal concentration using three-dimensional excitation–emission matrix (3D-EEM) fluorescence spectroscopy. In this study, we proposed a novel algal fluorescence anti-interference network (AFAI-Net) based on a convolutional neural network. The main procedure of this model can be divided into two parts: (1) to quickly determine if there is an interference of CDOM or turbidity in the detected algal samples; (2) to correct the interfered samples and output the fluorescent components of the algae. We trained the model using the 3D-EEMs of pure algal samples (non-interfered) and mixed samples of algae and CDOM or turbidity (interfered); as a result, the well-trained model achieved a total classification accuracy of 96.82%, and the RMSE of CDOM and turbidity removal fitting effects were 0.2274 and 0.3423, respectively. Compared with the non-negative weighted least squares (NNLS) regression analysis method, using the CNN model for CDOM correction resulted in 13.11%, 0.65%, and 5.69% reductions in the average deviation rate for PD, PG, and CM, respectively. Furthermore, the spectra corrected by the model predicted algal densities that were closer to the true algal densities. This study provides a new way to remove non-algal factors that affect algal fluorescence spectra in water bodies, which is beneficial to monitoring eutrophication and red tide in aquatic systems.

Non-line-of-sight (NLOS) imaging technology has shown potential in several applications, such as intelligent driving, warfare and reconnaissance, medical diagnosis, and disaster rescue. However, most NLOS imaging systems are expensive and have a limited detection range, which hinders their utility in real-world scenarios. To address these limitations, we designed an NLOS imaging system, which is capable of long-range data acquisition. We also introduce an NLOS object imaging method based on deep learning, which makes use of long-range projected images from different light fields to reconstruct hidden objects. The method learns the mapping relationships of projected images and objects and corrects the image structure to suppress the generation of artifacts in order to improve the reconstruction quality. The results show that the proposed method produces fewer artifacts in reconstructions, which are close to human subjective perception. Furthermore, NLOS targets can be reconstructed even if the distance between the detection device and the intermediate surface exceeds 50 m.

In the underwater environment, conventional hyperspectral imagers for imaging target scenes usually require stable carrying platforms for completing push sweep or complex optical components for beam splitting in long gaze imaging, which limits the system’s efficiency. In this paper, we put forward a novel underwater hyperspectral imaging (UHI) system inspired by the visual features of typical cephalopods. We designed a visual bionic lens which enlarged the chromatic blur effect to further ensure that the system obtained blur images with high discrimination of different bands. Then, chromatic blur datasets were collected underwater to complete network training for hyperspectral image reconstruction. Based on the trained model, our system only required three frames of chromatic blur images as input to effectively reconstruct spectral images of 30 bands in the working light range from 430 nm to 720 nm. The results showed that the proposed hyperspectral imaging system exhibited good spectral imaging potential. Moreover, compared with the traditional gaze imaging, when obtaining similar hyperspectral images, the data sampling rate in the proposed system was reduced by 90%, and the exposure time of required images was only about 2.1 ms, reduced by 99.98%, which can greatly expand its practical application range. This experimental study illustrates the potential of chromatic blur vision for UHI, which can provide rapid response in the recognition task of some underwater dynamic scenarios.