Low-power laser image enhancement via deep feature recovery for HDR 3D measurement.

Laser 3D measurement has gained widespread applications in industrial metrology . Still, it is usually limited by surfaces with high dynamic range (HDR) or the colorful surface texture of measured surfaces, such as metal and black industrial parts. Currently, conventional methods generally work with relatively strong-power laser intensities, which could potentially damage the sample or induce eye-safety concerns. For deep-learning-based methods, due to the different reflectivity of the measured surfaces, the HDR problem may require cumbersome adjustment of laser intensity in order to acquire enough training data. Even so, the problem of inaccurate ground truth may occur. To address these issues, this paper proposes the deep feature recovery (DFR) strategy to enhance low-light laser stripe images for achieving HDR 3D reconstruction with low cost, high robustness, and eye safety. To the best of our knowledge, this is the first attempt to tackle the challenge of high measurement costs associated with measuring HDR surfaces in laser 3D measurement. In learning the features of low-power laser images, the proposed strategy has a superior generalization ability and is insensitive to different low laser powers and variant surface reflectivity. To verify this point, we specially design the experiments by training the network merely using the diffusely reflective masks (DRM951) and testing the performance using diffusely reflective masks, metal surfaces, black industrial parts (contained in the constructed datasets DRO690, MO191, and BO107) and their hybrid scenes. Experimental results demonstrate that the proposed DFR strategy has good performances on robustness by testing different measurement scenes. For variously reflective surfaces, such as diffusely reflective surfaces, metal surfaces, and black parts surfaces, the reconstructed 3D shapes all have a similar quality to the reference method.

High dynamic range 3D measurement based on polarization and multispectrum co-modulation.

Three-dimensional (3D) shape measurement serves an important role in many areas, and fringe projection profilometry (FPP) is a widely used 3D measurement technique due to its non-physical contact and high speed. The real measurement scenarios are often mixtures of specular and diffuse reflections, causing overexposed and underexposed areas to co-exist. Currently, utilizing FPP to simultaneously measure overexposed and underexposed areas remains a challenge. To solve this problem, we propose a mixed reflection model and what we believe to be a novel high dynamic range (HDR) 3D measurement method based on polarization and multispectrum co-modulation. In mixed reflection, the fringe images captured by the polarized color camera can be modulated to different intensities between different channels due to the co-modulation effect. By synthesizing all sub-images, high-modulation fringe images are formed and simultaneous reconstruction of overexposed and underexposed surfaces is finally achieved. Compared to conventional methods, the proposed method is more effective for measuring complex reflection situations, especially when objects with specular and diffuse surfaces simultaneously exist in the scene. And what we believe to be a novel no-registration-error calibration framework for multi-channel cameras has also been proposed, which both acquires a significant amount of information in the region with HDR problems and avoids the registration error due to the physical distances between different channels. Experiments were conducted to verify the effectiveness of the proposed method.

A master-slave surveillance system to acquire panoramic and multiscale videos.

This paper describes a master-slave visual surveillance system that uses stationary-dynamic camera assemblies to achieve wide field of view and selective focus of interest. In this system, the fish-eye panoramic camera is capable of monitoring a large area, and the PTZ dome camera has high mobility and zoom ability. In order to achieve the precise interaction, preprocessing spatial calibration between these two cameras is required. This paper introduces a novel calibration approach to automatically calculate a transformation matrix model between two coordinate systems by matching feature points. In addition, a distortion correction method based on Midpoint Circle Algorithm is proposed to handle obvious horizontal distortion in the captured panoramic image. Experimental results using realistic scenes have demonstrated the efficiency and applicability of the system with real-time surveillance.

Anticorrosion Performance of Waterborne Coatings with Modified Nanoscale Titania under Subtropical Maritime Climate.

Steel structures located in subtropical marine climates face harsh conditions such as strong sunlight and heavy rain, and they are extremely corroded. In this study, a waterborne coating with excellent corrosion resistance, hydrophobic ability, high-temperature resistance and high density was successfully prepared by using modified nanoscale titania powders and grafted polymers. The effects of three modifiers on titania nanoparticles and waterborne coatings' properties were studied independently. The experimental results showed that the activation index of the modification employing methacryloxy silane reached 97.5%, which achieved the best modification effect at 64.4 °C for 43.3 min. The waterborne coating with nanoscale titania modified by methacryloxy silane exhibited the best hydrophobic effect, with a drop contact angle of 115.4° and excellent heat resistance of up to 317.2 °C. The application of the waterborne modified coating in steel structures under subtropical maritime climates showed that the waterborne titania coatings demonstrated excellent resistance to corrosion, high temperatures and harsh sunlight, with a maximum service life of up to five years. Economic analysis indicated that, considering a conservative three-year effective lifespan, this coating could save more than 50% in cost compared with conventional industrial coatings. Finally, the strengthening mechanism of the polymer coatings with modified nanoscale titania was analyzed.

Video denoising based on a spatiotemporal Kalman-bilateral mixture model.

We propose a video denoising method based on a spatiotemporal Kalman-bilateral mixture model to reduce the noise in video sequences that are captured with low light. To take full advantage of the strong spatiotemporal correlations of neighboring frames, motion estimation is first performed on video frames consisting of previously denoised frames and the current noisy frame by using block-matching method. Then, current noisy frame is processed in temporal domain and spatial domain by using Kalman filter and bilateral filter, respectively. Finally, by weighting the denoised frames from Kalman filtering and bilateral filtering, we can obtain a satisfactory result. Experimental results show that the performance of our proposed method is competitive when compared with state-of-the-art video denoising algorithms based on both peak signal-to-noise-ratio and structural similarity evaluations.