Range-gated Imaging Research Articles

Polarization-Enhanced Underwater Laser Range-Gated Imaging for Subaquatic Applications.

Laser range-gated underwater imaging technology, by removing most of the backscattering noise, can effectively increase image contrast and extend the detection range. The optical signal captured by a range-gated imaging system primarily comprises reflected light from the object and backscattered light from the surrounding water. Consequently, surfaces with low reflectivity or highly turbid water environments substantially constrain the applicability of the range-gated imaging system. To enhance the detection capability of underwater laser range-gated imaging, this paper proposes the incorporation of underwater polarized light imaging technology as an enhancement method. Based on polarization differences, backscattered light and reflected light from an object can be distinguished. Experimental results indicate that, compared to images obtained using a conventional range-gated laser imaging system, those captured with a polarization-enhanced system exhibit an increase of up to 47% for the corresponding Enhancement Measure Evaluation (EME) index. The proposed approach, which integrates polarization imaging with range-gated laser imaging, has the potential to broaden the applicability of underwater laser imaging scenarios, such as deep-sea exploration and military applications.

Depth-prior-based range-gated 3D imaging method: integration of optical property into visual learning

3D Range-gated Imaging (3DRGI) has great potential for long-range detection in adverse weather conditions. Recently, vision-guided 3DRGI has brought new perspectives to this area as it overcomes hardware limitations and greatly increases flexibility. However, existing vision-guided methods do not consider the optical properties of range-gated imaging, which results in low accuracy. This paper proposes a depth-prior-based 3DRGI method to combine the advantages of optical-based and vision-guided methods. In this method, depth-prior is firstly deduced from principles of range-gated imaging and provides effective depth signals. Then, adaptive depth intervals are estimated using statistical methods, and a depth-prior-guided loss function is designed. The integration of the depth-prior-guided loss function within a vision-guided model enables focused attention on pixels with depth estimations that are inconsistent with the depth prior, thereby refining the overall accuracy of the depth map. To prove the feasibility of the proposed method, comparison experiments and ablation studies have been performed. The results show that the root mean square error improved by more than 12% under adverse weather conditions.

Depth-prior-based LiDAR point cloud de-noising method leveraging range-gated imaging.

Light Detection and Ranging (LiDAR) has been widely adopted in modern self-driving vehicles and mobile robotics, providing 3D information of the scene and surrounding objects. However, LiDAR systems suffer from many kinds of noise, and its noisy point clouds degrade downstream tasks. Existing LiDAR point cloud de-noising methods are time-consuming or cannot deal with the noise caused by occlusions or penetrating transparent surfaces. In this paper, we introduce a depth-prior-based LiDAR point clouds de-noising method to deal with all types of noise in LiDAR point clouds in real time. The depth prior is derived from the fundamental principles of range-gated imaging and divides the depth of field into three parts, which can provide an effective depth signal. The LiDAR point cloud, which is acquired in synchronization with gated images, is projected into a depth map, and points whose depth is inconsistent with the depth prior can be regarded as noise and can be removed, finally. We conduct an ablation study and compare the proposed method with existing de-noising methods using the Gated2Depth dataset, which is to our knowledge the first long-range-gated dataset specifically designed for 3D detection in adverse weather conditions and includes all the necessary data. The results demonstrated that the proposed method achieves superior performance across all metrics.

Time dilation and time reversal with the multiple-wavelengths range-gated active imaging principle

The possibility of realizing time dilation and time reversal of events taking place in a scene by using the multiple-wavelengths range-gated active imaging (WRAI) principle in superimposed style was studied. Both temporal behaviors could be analyzed as a function of time since the WRAI principle allows different positions of the object in the image to be frozen at different moments according to the wavelengths. As the speed of the photons varies in the function of the refraction law of the crossed medium, different media were used to intervene in the time of the events recorded by the camera. Different wavelengths were used to select these media. By increasing the refractive index of the crossed medium as a function of time, the scene events arrived chronologically with an increasing delay compared to the events seen in the open, giving the impression of slowing down time. Similarly, by decreasing the refractive index of the crossed medium as a function of time, the scene events arrived chronologically in the opposite direction compared to the events seen in the open, giving the impression of going back in time. Experimental test results validated the theoretical part and the possibility of observing these different temporal behaviors with the multiple-wavelengths range-gated active imaging principle in superimposed style.

Range-Intensity-Profile-Guided Gated Light Ranging and Imaging Based on a Convolutional Neural Network.

Three-dimensional (3D) range-gated imaging can obtain high spatial resolution intensity images as well as pixel-wise depth information. Several algorithms have been developed to recover depth from gated images such as the range-intensity correlation algorithm and deep-learning-based algorithm. The traditional range-intensity correlation algorithm requires specific range-intensity profiles, which are hard to generate, while the existing deep-learning-based algorithm requires large number of real-scene training data. In this work, we propose a method of range-intensity-profile-guided gated light ranging and imaging to recover depth from gated images based on a convolutional neural network. In this method, the range-intensity profile (RIP) of a given gated light ranging and imaging system is obtained to generate synthetic training data from Grand Theft Auto V for our range-intensity ratio and semantic network (RIRS-net). The RIRS-net is mainly trained on synthetic data and fine-tuned with RIP data. The network learns both semantic depth cues and range-intensity depth cues in the synthetic data, and learns accurate range-intensity depth cues in the RIP data. In the evaluation experiments on both a real-scene and synthetic test dataset, our method shows a better result compared to other algorithms.

Speed Measurement of the Moving Targets Using the Stepping Equivalent Range-Gate Method.

In this paper, we proposed a stepping equivalent range-gate method (S-ERG method) to measure the speed and the distance of the moving target for range-gated imaging lidar. In this method, the speed is obtained by recording the time at which the moving target passes the front and back edges of the range gate, the distance information can also be obtained by the front and back edges of the range gate at the same time. To verify the feasibility of this method, a stationary target and a moving target with different speeds were measured by the S-ERG method. By using the S-ERG method, we not only obtained the distance information of the stationary target and the moving target at the front and back edges of the range gate, respectively, but also obtained the speed of the moving target. Compared to speeds measured by rotational displacement sensors, the speed measurement error of the S-ERG method is less than 5%, whether the target is far away or close to the range-gated lidar system, and this method is almost independent of the delay step time. The theoretical analysis and experimental results indicate range-gated imaging lidar using the S-ERG method has high practicality and wide applications.

Multiple-wavelength range-gated active imaging principle integrating spectral information for five-dimensional imaging.

The combined multiple-wavelength range-gated active imaging (WRAI) principle is able to determine the position of a moving object in a four-dimensional space and to deduce its trajectory and its speed independently of the video frequency. By combining two wavelength categories, it determines the depth of moving objects in the scene with the warm color category and the precise moment of a moving object's position with the cold color category. Therefore, since each object had the ability to transmit information from different wavelengths, related to the spectral reflectances, it became interesting to identify their spectral signatures from these reflectances. Using a conventional method of spectral classification, it was shown that it is possible to identify objects in a 3D scene from their a priori known spectral signatures and, thanks to this, to reveal especially the fifth dimension in the imaging of the WRAI principle. The experimental tests confirmed that it is possible to record moving objects in a five-dimensional space represented by a single image, thus validating this multi-wavelength imaging method.

Advances of laser range-gated three-dimensional imaging (invited)

Advances of laser range-gated three-dimensional imaging (invited)

Influence of optical polarization on underwater range-gated imaging for target recognition distance under different water quality conditions

Influence of optical polarization on underwater range-gated imaging for target recognition distance under different water quality conditions

Vision-guided three-dimensional range-gated imaging based on epistemic uncertainty estimation

Vision-guided three-dimensional range-gated imaging based on epistemic uncertainty estimation

Radiometry and contrast-to-noise ratio for continuous-wave and laser range-gated active imaging systems

Resolution and sensitivity must be considered in the design of an active imaging system. System sensitivity is characterized by the signal-to-noise or contrast-to-noise ratio and is derived through radiometry. We present a tutorial for the radiometry associated with the contrast-to-noise ratio for active continuous-wave and laser range-gated imaging systems, giving a useful metric for determining reflective-band sensor performance against a target and background. A calculation of the full power and contrast-to-noise ratio terms is shown for an example case, and all relevant radiometric signal terms are covered while describing the assumptions made. Coherent effects on signal-to-noise ratio are excluded from this analysis.

Three-dimensional super-resolution range-gated imaging based on Gaussian-range-intensity model.

Current range-gated imaging methods based on triangular or trapezoidal models face significant limitations in terms of the accuracy and precision of three-dimensional (3D) reconstruction. Here, we establish a range-gated 3D super-resolution image reconstruction method based on a Gaussian-range-intensity model. A denoised optimization method for image preprocessing is established to improve the distance accuracy of the reconstruction. The experimental results show that the model can reduce the error of target distance by 64% and possess the reconstruction of multiple kinds of targets with a distance resolution of 1.5cm and lower noise, realizing better reconstruction results compared with the existing range-gated 3D imaging techniques.

Target acquisition performance comparison of active continuous-wave and range-gated imaging systems at 1.6 and 2.1 μm

Target acquisition performance comparison of active continuous-wave and range-gated imaging systems at 1.6 and 2.1 μm

Active imaging through dense fog by utilizing the joint polarization defogging and denoising optimization based on range-gated detection.

Imaging through the scattering medium, such as fog, is important for military and civilian applications. However, the fog concentration restricts the current defogging methods; the image will be seriously degraded in dense fog scenes. Here, an imaging technique by developing joint active polarization defogging and denoising optimization methods based on range-gated detection is proposed for the target in fog conditions. The range-gated imaging method shields the scattering light from outside the selected region to improve the signal intensity. The properties of signal light, backscattering light, and forward scattering light in the range-gated imaging way are analyzed experimentally and theoretically. Thus the elimination method of backscattering light is developed in terms of polarization differences in the degree of polarization and angle of polarization, and the block-matching with 3D transform-domain collaborative filtering (BM3D) algorithm is developed to remove the effect of the forward scattering light on the image. By adopting the proposed defogging method, the clear imaging of the target under fog with an optical thickness of up to 5 is realized, and the target contour and detail information are successfully recovered. Compared with the complete failure of the current defogging method, this method can recover targets with high contrast and signal-to-noise ratio in dense fog scenes, which exhibits widespread application potential for target detection and recognition in severe weather and turbid underwater environment.

Laser-Visible Face Image Translation and Recognition Based on CycleGAN and Spectral Normalization.

The range-gated laser imaging instrument can capture face images in a dark environment, which provides a new idea for long-distance face recognition at night. However, the laser image has low contrast, low SNR and no color information, which affects observation and recognition. Therefore, it becomes important to convert laser images into visible images and then identify them. For image translation, we propose a laser-visible face image translation model combined with spectral normalization (SN-CycleGAN). We add spectral normalization layers to the discriminator to solve the problem of low image translation quality caused by the difficulty of training the generative adversarial network. The content reconstruction loss function based on the Y channel is added to reduce the error mapping. The face generated by the improved model on the self-built laser-visible face image dataset has better visual quality, which reduces the error mapping and basically retains the structural features of the target compared with other models. The FID value of evaluation index is 36.845, which is 16.902, 13.781, 10.056, 57.722, 62.598 and 0.761 lower than the CycleGAN, Pix2Pix, UNIT, UGATIT, StarGAN and DCLGAN models, respectively. For the face recognition of translated images, we propose a laser-visible face recognition model based on feature retention. The shallow feature maps with identity information are directly connected to the decoder to solve the problem of identity information loss in network transmission. The domain loss function based on triplet loss is added to constrain the style between domains. We use pre-trained FaceNet to recognize generated visible face images and obtain the recognition accuracy of Rank-1. The recognition accuracy of the images generated by the improved model reaches 76.9%, which is greatly improved compared with the above models and 19.2% higher than that of laser face recognition.

Multiple-wavelength range-gated active imaging applied to the evaluation of simultaneous movement of millimeter-size objects moving in a given volume.

The combined multiple-wavelength range-gated active imaging (WRAI) principle is able to determine the position of a moving object in a four-dimensional space and to deduce its trajectory and its speed independently of the video frequency. However, when the scene size is reduced and the objects have a millimeter size, the temporal values intervening on the depth of the visualized zone in the scene cannot be reduced further because of technological limitations. To improve the depth resolution, the illumination type of the juxtaposed style of this principle has been modified. Therefore, it was important to evaluate this new context in the case of millimeter-size objects moving simultaneously in a reduced volume. Based on the rainbow volume velocimetry method, the combined WRAI principle was studied in accelerometry and velocimetry with four-dimensional images of millimeter-size objects. This basic principle combining two wavelength categories determines the depth of moving objects in the scene with the warm color category and the precise moment of moving objects position with the cold color category. The difference in this new, to the best of our knowledge, method is at the level of the scene illumination, which is obtained transversally by a pulsed light source having a wide spectral band limited in warm colors in order to get a better depth resolution. For cold colors, the illumination with pulsed beams of distinct wavelengths remains unchanged. Thus, it is possible independently of the video frequency to know from a single recorded image the trajectory, the speed, and the acceleration of millimeter-size objects moving simultaneously in 3D space and also the chronology of their passages. The experimental tests validated this modified multiple-wavelength range-gated active imaging method and confirmed the possibility of avoiding confusion when the object trajectories intersect.

Image enhancement for underwater range-gated image with multi-slice integration method

Active illumination underwater brings the backscatter problem. The traditional pulsed laser range-gated imaging (PLRGI) system uses a small gate width to suppress most of the backscattered light. As a result, the depth of field (DOF) of the PLRGI system is small. In addition, the DOF has to be extended when the system is used for searching the target. There are several methods to extend the DOF. However, every method has its shortcoming. To overcome these problems, a multi-slice integration (MSI) method is proposed. In turbid water, the image captured by the MSI method also has the backscatter problem. The rolling ball and intensity transformation are proposed as an image enhancement method to remove the backscattered light and improve the brightness of the image. Experiments carried out in a towing boat tank and Mulan Lake show that the proposed method is better than lower-upper-threshold correlation, dark channel priors, and unsharp filtering for backscattered light removal.

Single Image Deblurring for Pulsed Laser Range-Gated Imaging System with Multi-Slice Integration

The multi-slice integration (MSI) method is one of the approachs to extend the depth of view (DOV) of the pulsed laser range-gated imaging (PLRGI) system. When the DOV is large enough and exceeds the depth of focus of the system, it may make some targets in the image clear and others blurred. In addition, forward scatter is also considered to have a blurring effect on the image. There is very little literature to solve the combined effect of forward scatter and defocus. An imaging model is built based on the model from Jaffe–McGlamery and Fourier optics. According to the imaging model, backscattered light is independent from reflected light from the target, and forward scatter has a relationship with the reflected light. Thus, backscattered light should be removed before deblurring. First, rolling ball and intensity transformation are used to remove the backscattered light and enhance the image. Then, a deep learning model based on Transformer is used to deblur the image. To enable the deep learning model to accommodate different degrees of blurred image, 16 different blur kernels are generated according to the imaging model. Sharp images from a DPDD dataset were chosen to train the model. Images of varying degrees of blur were collected from a water tank and a boat tank by the PLRGI system as test sets. Image deblurring results show that the proposed method can remove different levels of blur and can deal with images which have sharp targets and blurred targets together.

Adjacent Frame Difference with Dynamic Threshold Method in Underwater Flash Imaging LiDAR

During the underwater LiDAR imaging process, the images achieved by the conventional constant threshold adjacent frame difference (AFD) method normally loses the distance information of targets. This is mainly due to the Gaussian distribution of the laser light intensity field, which leads to the inhomogeneous intensity distribution in the frame from the target acquired by intensity charge-coupled devices (ICCD). In order to overcome this issue, the novel dynamic threshold adjacent frame difference (DTAFD) method was proposed in this paper. The DTAFD method modifies the intensity threshold following the pixel intensities in the different parts of the single frame intensity image acquired by ICCD. After the detailed theoretical demonstration of the DTAFD method, with the purpose of verifying its feasibility, the self-developed range-gated flash imaging LiDAR has been employed to construct the distance images of the rectangular and circular shaped targets at different distances. The distance between the rectangular target and the LiDAR system is 25.7 m, and the circular target is 70 cm further away from the rectangular target. The full distance information of these two targets is obtained by the DTAFD method with an effectively suppressing noise and the PSNR is increased from 6.95±0.0426 dB to 7.62±0.0264 dB. The experimental results indicate that the DTAFD method efficiently solves the AFD method’s drawback on the target information loss caused by the unequal optical field distribution, which makes it more suitable for the scenarios with uneven laser distribution such as the underwater imaging environment.

Range Intensity Profiles of Multi-Slice Integration for Pulsed Laser Range-Gated Imaging System

Pulsed laser range-gated imaging (PLRGI) is one of the most effective methods to achieve underwater high-resolution imaging. When searching target, there are two methods can be used: fixed gate and sliding gate. In practice, fixed gate has a small depth of field of view and sliding gate cannot meet the need of real time. In order to overcome these problems, multi-slice integration (MSI) method is proposed in this paper. First, the laser energy received by the PLRGI system is derived from radiative transfer theory. In addition, range intensity profiles (RIP) of MSI method is established. Experiments are carried out in lab to validate the RIP model and results show that theoretical data and experimental data are in good coincidence. Then the gate width and the number of pulse assigned to each slice are discussed. Finally, to prove the effectiveness of the MSI method, experiments are carried out in a boat tank. Results show that the MSI method is better than the fixed gate method for surveillance, and can acquire a clear image of the target at 18 m (4.5 times of the attenuation length) in the water with attenuation coefficient of 0.25 m−1.