The turbine blade is a key component in an aeroengine. Currently, measuring the turbine blade radiation temperature always requires obtaining the emissivity of the target surface in advance. However, changes in the emissivity and the reflected ambient radiation cause large errors in measurement results. In this paper, a three-wavelength radiation temperature measurement method was developed, without known emissivity, for reflection correction. Firstly, a three-dimensional dynamic reflection model of the turbine blade was established to describe the ambient radiation of the target blade based on the real surface of the engine turbine blade. Secondly, based on the reflection correction model, a three-wavelength radiation temperature measurement algorithm, independent of surface emissivity, was proposed to improve the measurement accuracy of the turbine blade radiation temperature in the engine. Finally, an experimental platform was built to verify the temperature measurement method. Compared with three conventional colorimetric methods, this method achieved an improved performance on blade temperature measurement, demonstrating a decline in the maximum error from 6.09% to 2.13% and in the average error from 2.82% to 1.20%. The proposed method would benefit the accuracy in the high-temperature measurement of turbine blades.

Due to the development of printing materials, light-cured 3D printing is playing an increasingly important role in industrial and consumer markets for prototype manufacturing and conceptual design due to its advantages in high-precision and high-surface finish. Despite its widespread use, it is still difficult to achieve the 3D printing requirements of large volume, high resolution, and high speed. Currently, traditional light-cured 3D printing technologies based on stereolithography, such as regular DLP and SLA, can no longer meet the requirements of the processing size and processing rate. This paper introduces a dynamic projection of 3D printing technology utilizing a digital micro-mirror device (DMD). By projecting the ultraviolet light pattern in the form of “animation”, the printing resin is continuously cured in the exposure process to form the required three-dimensional structure. To print large-size objects, the three-dimensional model is sliced into high-resolution sectional images, and each layer of the sectional image is further divided into sub-regional images. These images are dynamically exposed to the light-curing material and are synchronized with the scanning motion of the projection lens to form a static exposure pattern in the construction area. Combined with the digital super-resolution, this system can achieve the layering and fine printing of large-size objects up to 400 × 400 × 200 mm, with a minimum feature size of 45 μm. This technology can achieve large-size, high-precision structural printing in industrial fields such as automobiles and aviation, promoting structural design, performance verification, product pre-production, and final part processing. Its printing speed and material bending characteristics are superior to existing DLP light-curing 3D printing methods.

Nowadays, wide-field of view plasmonic structured illumination method (WFPSIM) has been extensively studied and experimentally demonstrated in biological researches. Normally, noble metal structures are used in traditional WFPSIM to support ultra-high wave-vector of SPs and an imaging resolution enhancement of 3-4 folds can be achieved. To further improve the imaging resolution of WFPSIM, we hereby propose a wide-field optical nanoimaging method based on a hybrid graphene on meta-surface structure (GMS) model. It is found that an ultra-high wave-vector of graphene SPs can be excited by a metallic nanoslits array with localized surface plasmon enhancement. As a result, a standing wave surface plasmons (SW-SPs) interference pattern with a period of 11 nm for a 980 nm incident wavelength can be obtained. The potential application of the GMS for wide-field of view super-resolution imaging is discussed followed by simulation results which show that an imaging resolution of sub-10 nm can be achieved. The demonstrated method paves a new route for wide field optical nanoimaging, with applications e.g. in biological research to study biological processes occurring in cell membrane.

The purpose of infrared and visible image fusion is to generate images with prominent targets and rich information which provides the basis for target detection and recognition. Among the existing image fusion methods, the traditional method is easy to produce artifacts, and the information of the visible target and texture details are not fully preserved, especially for the image fusion under dark scenes and smoke conditions. Therefore, an infrared and visible image fusion method is proposed based on visual saliency image and image contrast enhancement processing. Aiming at the problem that low image contrast brings difficulty to fusion, an improved gamma correction and local mean method is used to enhance the input image contrast. To suppress artifacts that are prone to occur in the process of image fusion, a differential rolling guidance filter (DRGF) method is adopted to decompose the input image into the basic layer and the detail layer. Compared with the traditional multi-scale decomposition method, this method can retain specific edge information and reduce the occurrence of artifacts. In order to solve the problem that the salient object of the fused image is not prominent and the texture detail information is not fully preserved, the salient map extraction method is used to extract the infrared image salient map to guide the fusion image target weight, and on the other hand, it is used to control the fusion weight of the basic layer to improve the shortcomings of the traditional ‘average’ fusion method to weaken the contrast information. In addition, a method based on pixel intensity and gradient is proposed to fuse the detail layer and retain the edge and detail information to the greatest extent. Experimental results show that the proposed method is superior to other fusion algorithms in both subjective and objective aspects.

Hyperspectral images are characterized by hundreds of spectral bands and rich information. However, there exists a large amount of information redundancy among adjacent bands. In this study, a spatial–spectral combination method for hyperspectral band selection (SSCBS) is proposed to reduce information redundancy. First, the hyperspectral image is automatically divided into subspaces. Seven algorithms classified as four types are executed and compared. The means algorithm is the most suitable for subspace division of the input hyperspectral image, with the calculation being the fastest. Then, for each subspace, the spatial–spectral combination method is adopted to select the best band. The band with the maximum information and more prominent characteristics between the adjacent bands is selected. The parameters of Euclidean distance and spectral angle parameters are used to measure the intraclass correlation and interclass spectral specificity, respectively. Weight coefficient quantifying the intrinsic spatial–spectral relationship of pixels are constructed, and then the optimal bands are selected by a combination of the weight coefficients and the information entropy. Moreover, an automatic method is proposed in this paper to provide an appropriate number of band sets, which is out of consideration for existing research. The experimental results show, as compared to other competing methods, that the SSCBS approach has the highest classification accuracy on the three benchmark datasets and takes less computation time. These demonstrate that the proposed SSCBS achieves satisfactory performance against state-of-the-art algorithms.

There are more than eight large aperture telescopes (larger than eight meters) equipped with adaptive optics system in the world until now. Due to the limitations such as the difficulties of increasing actuator number of deformable mirror, most of them work in the infrared waveband. A novel two-step high-resolution optical imaging approach is proposed by applying phase diversity (PD) technique to the open-loop liquid crystal adaptive optics system (LC AOS) for visible light high-resolution adaptive imaging. Considering the traditional PD is not suitable for LC AOS, the novel PD strategy is proposed which can reduce the wavefront estimating error caused by non-modulated light generated by liquid crystal spatial light modulator (LC SLM) and make the residual distortions after open-loop correction to be smaller. Moreover, the LC SLM can introduce any aberration which realizes the free selection of phase diversity. The estimating errors are greatly reduced in both simulations and experiments. The resolution of the reconstructed image is greatly improved on both subjective visual effect and the highest discernible space resolution. Such technique can be widely used in large aperture telescopes for astronomical observations such as terrestrial planets, quasars and also can be used in other applications related to wavefront correction.