In order to expand application of uncooled short wave infrared array detectors for low-light night vision， a research on low-light imaging of short-wave infrared have carried out. This paper proposes a new image enhancement method to suppress image noise， enhance image details and improve image quality. The proposed schemes use 3DNR （3D noise reduction）， combine the multi-scale Gaussian differential method with the edge preserving filter to separate the high-frequency information and hidden noise of the image to the maximum extent， and then carry out the adaptive grayscale mapping for the image. The experimental results demonstrate that the proposed algorithm outperforms some state-of-the-art algorithms， and it can achieve outstanding image enhancement performance and suppress the time-domain noise of the image under low-light illumination.

To ensure the fusion quality and efficiency simultaneously， a novel image fusion method based on multi-scale Gaussian filtering and morphological transform is proposed. The multi-scale Gaussian filtering is designed to decompose the source images into a series of detail images and approximation images. The multi-scale top- and bottom-hat decompositions are used respectively to fully extract the bright and dark details of different scales in each approximation image. The multi-scale morphological inner- and outer-boundary decompositions are constructed to fully extract boundary information in each detail image. Experimental results demonstrate that the proposed method is comparable to or even better in comparison with typical multi-scale decomposition-based fusion methods. Additionally， the method operates much faster than some advanced multi-scale decomposition-based methods like NSCT and NSST.

The detection of the aerial targets applied on the space-based platform has disadvantages of long distance， weak signal of targets， complex background clutters and detector noises. Therefore， to obtain the strongest signal of the target， it is crucial to determine the bands used during the process of detection. The trapezoidal plume and the cone plume simulation model are systematically established， meanwhile an SNCR method combining the signal to noisy ratio（SNR） and the signal to clutter ratio（SCR） is proposed to determine the detection bands. The experimental result shows that， different aerial targets have different SNCR values， but with the same peak value. According to the method mentioned above， bands have been determined and eventually the values are as follows：3.7~4.15 μm in MWIR band in which interval is not less than 0.3 μm，8~12 μm in LWIR band in which interval is not less than 0.3 μm.

In the complex and changeable sea environment， when using infrared imaging technology to search and rescue small and medium targets on the sea surface， it is necessary to classify the collected original images in order to facilitate the subsequent target processing in different scenes. According to different environmental conditions， the sea infrared images are divided into five kinds of scenes. The training set images are extracted from two aspects： one is to divide an image into basic layer and detail layer by the Gaussian filter， and use improved histogram of oriented gradient （HOG） method to extract the features； the other is to extract features by calculating local contrast of images. The extracted feature vectors are fused and input into the classifier， and the test set images are classified by support vector machine （SVM）. In this paper， a new feature descriptor combined with HOG and local contrast method （LCM） is used to classify the scene of sea infrared image. Compared with other methods， the results show that the accuracy of the improved method is 96.4%， which reflects the feasibility and effectiveness.

Aiming at the fusion of infrared intensity-polarization image, an intelligent fusion method based on spatially weighted averaging method optimized by fireworks algorithm is proposed. Based on the optimization model, the boundary conditions of fireworks algorithm are determined. The fitness function based on comprehensive relative-entropy is established by introducing the weight of relative-entropy. Finally, the fusion experiments on three groups of infrared image “ground”, “truck” and “car” are carried out with six typical traditional fusion methods, and the fusion results are evaluated objectively and compared with the visual effects. The experimental results show that the proposed method can effectively achieve the fusion of infrared intensity map and polarization map, and retain the infrared intensity and polarization characteristics. Combining the visual effect and objective evaluation results, the method in this paper is superior to the comparison algorithm in relative-entropy, similarity of summary structure and total mutual information index.

In order to effectively detect weak and small infrared targets under complex background, a single-frame method based on horizontal-vertical multi-scale grayscale difference （HV-MSGD） is proposed to enhance weak targets, and the strong edges of background are suppressed by the difference between the distance and grayscale values. There is discontinuity between the target area and the surrounding area. To strengthen their differences, HV-MSGD combined with bilateral filtering （BF） can increase the intensity of the target while suppressing the background. Candidate targets are further extracted by adaptive local threshold segmentation and global threshold segmentation. In order to further verify the impact on single-frame detection, the above-mentioned single-frame detection algorithm is combined with an improved untraced Kalman particle filter （UPF） to implement trajectory detection. The experimental results show that this method is better than other methods under weak signal-to-noise ratio （SNR）. It can enhance the target while suppressing the background, and the enhancement effect is 6-30 times that of other methods. In the experiments, the input signal-to-noise ratios were 2.78, 1.77, 1.79, 1.13, and 1.16, respectively. After image processing, the background suppression factors （BSFs） are 13.48, 21.33, 11.73, 20.63, and 121.92, and the signal-to-noise ratio gains （GSNRs） are 40.09, 71.37, 27.53, 12.65, and 131, respectively. The probability of detection （Pd） of this method is also superior to other algorithms. When the false alarm rates （FARs） are 5×10-4, 1×10-3, 1×10-3, 1×10-5, and 7×10-6, the Pd values of the five sets using real sequence images are calculated to be 94.4%, 92.2%, 91.3%, 95.6% and 96.7% respectively.

Due to the limitations of infrared optical diffraction and infrared detectors, the noise of infrared images is relatively large and the resolution is low. Super-resolution reconstruction of infrared images improves image resolution, but at the same time enhances the noise of background. Aiming at this problem, a salience region super-resolution reconstruction algorithm for infrared images based on sparse coding is proposed. By combining the saliency detection and the super-segment reconstruction, it improves the target definition and reduces the background noise. Firstly, image feature is extracted by double-layer convolution, and image patches with large entropy are adaptively selected for training the joint dictionary. Sparse features are used to calculate the saliency to obtain salient regions, which reconstructs image patches in saliency region by the trained dictionary while the background region adopts Gaussian filtering. Experimental results show that the improved reconstruction algorithm is better than ScSR and SRCNN under the same conditions. The image signal-to-noise ratio is increased by 3-4 times.

Processing ground penetrating radar data to obtain well-focused images for object detection has been an active research area. Phase-shift migration (PSM) is a widely used method since it allows the wave velocity to vary with respect to multi-layer medium. However, this requires pixel-by-pixel calculation of the image, which is time-consuming. This paper presents an extended Omega-k algorithm for multi-layer medium imaging with significantly less computation complexity than the PSM algorithm. The extended Omega-k exploits fast interpolation in the wave-number domain instead of iterative calculating as done by PSM. The method of estimating the wave propagation velocity in different media is also proposed via vertex region extraction for phase compensation and image focusing. Various images of buried targets of a two-layer medium experiment are obtained, which validate the effectiveness of the proposed algorithm, and make it practical for some typical ground-based surveillance applications.

To study the guard-ring suppression effect on the extension of the photo-sensitive area in planar-type front illuminated InP/InGaAs hetero-structure detector, the InGaAs photo detectors with different distances between guard-ring and PN junction were designed and fabricated. The actual distance between guard-ring and PN junction of the detector was calculated based on the atomic force microscopy (AFM) and scanning capacitance microscopy (SCM) measurements. The characteristics of the photo response of the detectors with guard-ring were carried out with laser beam induced current (LBIC) method. It was indicated that LBIC signal of the photo detector without guard-ring fit well with the exponential decay function, while that of detector with guard-ring followed the Gaussian distribution. The extension value of the photo-sensitive area decreased linearly as the distance between the guard-ring and PN junction decreased. It was concluded that the appropriate gap between the guard-ring and PN junction should be in the range of 7 to 12μm.

A novel infrared target extraction algorithm based on particle swarm optimization particle filter (PSOPF) was proposed. The problem of infrared target extraction was analyzed and solved in the view of state estimation. In the framework of particle filter, the threshold state space on the gray-variance weighted information entropy and the gray value of each pixel was established. Particle swarm optimization was introduced to construct the state transition model. The observation model based on extraction results evaluation function was constructed, which integrated gray, entropy, gradient and spatial distribution of pixels. Finally, the weighted average of all the particles was used as target extraction threshold. The experiment results prove that the proposed algorithm is effective and robust.

Water clouds, which are distributed at the bottom of atmosphere and composed of spherical water droplets, play an important role in Earth's radiation balance and global climate change. The effects of water clouds are based on their microphysical and optical properties. Based on the studies of water clouds optical properties in 0.865μm, the sensitivity of normalized radiance and polarized radiance intensities to water clouds optical parameters, such as effective radius, optical thickness and surface albedo, was evaluated by using vector radiative transfer model. The simulated results indicate that the information of multidirectional polarized radiance can show the microphysical and optical properties of water clouds effectively, which can be used to retrieve the properties of water clouds. This study provides the basis for using the remote sensing data of multi-angular polarization to retrieve the properties of water clouds was proposed.