Blind image restoration algorithms for motion blur have been deeply researched in the past years. Although great progress has been made, blurred images containing large blur and rich, small details still cannot be restored perfectly. To deal with these problems, we present a robust image restoration algorithm for motion blur of general image sensors in this paper. Firstly, we propose a self-adaptive structure extraction method based on the total variation (TV) to separate the reliable structures from textures and small details of a blurred image which may damage the kernel estimation and interim latent image restoration. Secondly, we combine the reliable structures with priors of the blur kernel, such as sparsity and continuity, by a two-step method with which noise can be removed during iterations of the estimation to improve the precision of the estimated blur kernel. Finally, we use a MR-based Wiener filter as the non-blind deconvolution algorithm to restore the final latent image. Experimental results demonstrate that our algorithm can restore large blur images with rich, small details effectively.

Non-line-of-sight (NLOS) detecting technology is used to locate and track a hidden object through scattered light after three reflections. This technology is expected to be applied in automatic driving, danger rescue, etc. Herein, we improve the real-time performance of NLOS detection in two aspects. Firstly, we introduce the method of dynamic programming to detect extremely weak signals from the hidden object based on current and previous information, which can unmix the target signal from noise at a shorter exposure time. Secondly, we simplify the positioning method by extracting the time difference from the histogram and training a feed-forward network that speeds up the process of spatial positioning. Based on the improvements, we demonstrated fast tracking experiments in a corridor 20 m away, tracking a moving hidden object at framerates of 50 Hz. These improvements make NLOS fast detection a step forward.

Non-line-of-sight (NLOS) technology has been rapidly developed in recent years, allowing us to visualize or localize hidden objects by analyzing the returned photons, which is expected to be applied to autonomous driving, field rescue, etc. Due to the laser attenuation and multiple reflections, it is inevitable for future applications to separate the returned extremely weak signal from noise. However, current methods find signals by direct accumulation, causing noise to be accumulated simultaneously and inability of extracting weak targets. Herein, we explore two denoising methods without accumulation to detect the weak target echoes, relying on the temporal correlation feature. In one aspect, we propose a dual-detector method based on software operations to improve the detection ability for weak signals. In the other aspect, we introduce the pipeline method for NLOS target tracking in sequential histograms. Ultimately, we experimentally demonstrated these two methods and extracted the motion trajectory of the hidden object. The results may be useful for practical applications in the future.

Recently, non-line-of-sight (NLOS) detection based on time of flight (TOF) has been investigated. In order to simulate the NLOS location of a hidden object, we derive the signal scattered by the object and build a model of photon flight based on photon scattering and propagation. To improve the authenticity of the model, the bidirectional reflectance distribution function (BRDF) is used to characterize the scattering process. The Gauss filter is proposed to extract the TOF of the scattering sources of interest without a priori information or manual judgment of the useful scattered signal by filtering the disturbance out of the histogram. The hidden object can then be located by TOF processing. Compared with previous work using a fitting algorithm, the Gauss filtering approach preserves more waveform information and presents improved positioning accuracy and robustness under the influence of noise, which is demonstrated in both simulation and experiment. It is possible to locate a NLOS object automatically through filtering identification of the object signal. The simplicity, high efficiency, and automation of this algorithm make it applicable for tracking a hidden moving object.

The Risley prism system offers advantages such as compact structure and excellent dynamic performance, making it suitable for installation on static and motion platforms for target acquisition, aiming, and tracking. This paper presents a strapdown line-of-sight (LOS) stabilization method for the Risley prism system on motion platforms. The method establishes the coordinate transformation between the Risley prism and the motion platform. Real-time platform attitude angles from an inertial measurement unit (IMU) are used to compute the direction cosine matrix, which, combined with the coordinate transformation, determines the target’s actual guided position in the Risley prism’s coordinate. The Risley prism’s rotational angles are then calculated based on the target’s actual guided position to ensure LOS stability and capture the target. After LOS stabilization, an image-based closed-loop tracking cascade control system that integrates a Risley prism and a fast steering mirror with a single image detector (IBCLTCR-F), is used to enable fast and high-precision target tracking. Experimental results demonstrate that the proposed method achieves disturbance rejection of −32.8 dB, −28.8 dB, and −17.3 dB for platform disturbances at 0.05 Hz, 0.2 Hz, and 0.5 Hz, respectively. Furthermore, compared to the Risley prism system, the IBCLTCR-F system improves the dynamic response capability of target tracking in the nonlinear region by a factor of 10 and reduces the tracking error by 70%.