LLL Image Research Articles

Effective enhancement method of low-light-level images based on the guided filter and multi-scale fusion.

Aiming to solve the problem of low-light-level (LLL) images with dim overall brightness, uneven gray distribution, and low contrast, in this paper, we propose an effective LLL image enhancement method based on the guided filter and multi-scale fusion for contrast enhancement and detail preservation. First, a base image and detail image(s) are obtained by using the guided filter. After this procedure, the base image is processed by a maximum entropy-based Gamma correction to stretch the gray level distribution. Unlike the existing methods, we enhance the detail image(s) based on the guided filter kernel, which reflects the image area information. Finally, a new method is proposed to generate a sequence of artificial images to adjust the brightness of the output, which has a better performance in image detail preservation compared with other single-input algorithms. Experiments show that the proposed method can provide a more significant performance in enhancing contrast, preserving details, and maintaining the natural feeling of the image than the state of the art.

Objective evaluation of integrated structural defects of low-light-level image intensifier based on local image patch extreme and frequency fast Fourier transform

Integrated structural defects are one of the most common defects in the microchannel plate fabrication and physicochemical treatment stage of low-light-level (LLL) image intensifiers. Its appearance seriously affects the imaging quality. The traditional evaluation method of such defects is solely based on visual observation, and its main disadvantages are strong subjectivity and large uncertainty. To address the above problems, two objective evaluation methods of integrated structural defects of an LLL image intensifier are introduced. The first is based on local image patch extreme, and the second is based on frequency fast Fourier transform. The basic idea of the first method is to scan the effective area (EA) of the target image globally and then extract the target defect by adopting a series of local image patch extremes adjacent in the Y-axis. Experimental results reveal that this method shows excellent performance in detecting and positioning accuracy while the time cost is satisfactory. The general steps of the second method are to scan the EA globally, and then perform a fast Fourier transform on the obtained image patch; through bandpass filtering in the frequency domain and inverse Fourier transforming the convoluted image, structures or objects projecting the similar characteristics of integrated structural defects are detected. This method not only maintains good detection integrity but also ensures high detection accuracy. Through experiments on several defective image intensifiers and compared with traditional relevant technologies, we conclude that the proposed methods can realize the high-precision objective detection of integrated structural defects of LLL image intensifiers and can be used as effective alternatives to the traditional evaluation method of such defects.

Automatic evaluation of vertex structural defects on the anode surface of a low-light-level image intensifier based on proposed individual image processing strategies.

In the process of microchannel plate (MCP) making and physicochemical treatment of a low-light-level (LLL) image intensifier, multifilament fixed pattern noise, also known as structural defects, is one of the most common defects in the anode surface. The appearance of this defect will seriously affect the imaging quality of an image intensifier, so it should be found in time before delivery. The traditional evaluation method of this defect relies on subjective judgment, and the disadvantage is that the division of the dense defect area and the measurement of defect gray difference (GD) are not standardized. To address this problem, an automatic evaluation method of vertex structural defects of an LLL image intensifier based on proposed individual image processing strategies is presented, which provides a digital evaluation scheme for such defects. This method is composed of two parts: quasi-circular defect detection and defect GD calculation. The first part is composed of coarse detection and fine detection. Coarse detection is to scan the anode surface and take the two ends of a pair of adjacent line segments with a large gradient sum and opposite gray change direction as the defect boundaries; fine detection is to establish the image patch from defect boundaries, extract the edge segment from the image patch, and judge whether it conforms to the shape of a circle. In order to substantiate the performance of the quasi-circular defect detection strategy, two relevant techniques are used as comparison. One is based on a Gaussian filter, and the other is based on a fixed-size window template. The comparison results show that our method, to the best of our knowledge, has the best detection performance for vertex structural defects. The second part consists of region of interest (ROI) cropping, secondary defect detection, shortest distance sequence establishment, effective distance extraction, triplet set construction, and triplet GD calculation. First, the location histogram of defects is established to cut ROI; then, the secondary defect detection is performed to extract more vertex structural defects from ROI; after that, the shortest distance sequence of defects is constructed, and the effective distances are extracted by using the structural features of multifilament. Finally, the triplet set is generated according to the effective distance, and the triplet GD is calculated based on the gray information near the triplet baseline. The GD of vertex structural defects corresponds to the maximum GD of triplets. So as to verify the effectiveness of vertex defect GD calculation strategy, several image tubes with different degrees of such defects are used for experiments, and the subjective evaluation method is used as comparison. The experimental results substantiate that this method is superior to the subjective method in locating ROI accurately and calculating defect GD quantitatively. In general, the automatic evaluation method can be regarded as an effective evaluation scheme for vertex structural defects of an LLL image intensifier.

Objective evaluation of low-light-level image intensifier resolution based on a model of image restoration and an applied model of image filtering

Resolution is one of the most important parameters of low-light-level (LLL) image intensifiers, reflecting the target detection performance of assembled night vision devices under 10−3 to 10−1 lx dark environment. The traditional methods of measuring this parameter are classified into subjective evaluation and objective test. The disadvantages of subjective evaluation are strong subjectivity and low accuracy, while the traditional objective test method shows the weaknesses of excessive human intervention and large time consumption. To address these problems, an objective evaluation method based on a model of image restoration and an applied model of image filtering is proposed, which makes use of the similarity between unit stripe pattern and standard stripe pattern to evaluate the recognizability of unit stripe pattern. Firstly, the region of interest (ROI) is cut out from the original image and preprocessed to acquire the clearer results. Then, a model of image restoration is utilized to recover the stripe information lost in image preprocessing. After that, individual unit images are segmented from ROI, laying the foundation for the calculation of unit stripe definition. To extract recognizable unit images from unit image set, an image filtering model composed of several discernible constraints are presented according to the similarity features between unit stripe pattern and standard stripe pattern. Finally, the definition of unit is represented by the weighted sum of the product of constraint score and proportion of the number of discernable images under the constraint to the total number of images in the unit. With the help of linear fitting algorithm, the resolution of LLL image intensifier is calculated by combining the unit resolution-definition correspondences with the limiting definition. To verify the effectiveness of this method, different types of image tubes are used for experiments, and the subjective evaluation method and the advanced objective evaluation technology are adopted as comparison. The experimental results demonstrate that the evaluation results of this method are in good agreement with the subjective judgment results, and the accuracy is higher, reflecting in the maximum deviation is 2.8 lp/mm. The time efficiency of this method is greatly improved compared with that of traditional objective evaluation method. Moreover, the repeatability experiment results reveal that the stability of this approach is superior to that of the cited techniques, and the maximum deviation of evaluation results is 1.2 lp/mm (the lowest). In general, this method can overcome the shortcomings of traditional subjective and objective evaluation methods, and provide an effective and feasible scheme for the standardized measurement of LLL image intensifier resolution.

Tunable-liquid-crystal-filter-based low-light-level color night vision system and its image processing method.

We introduce and verify a single-channel time-division filtering low-light-level (LLL) color night vision system (3LCNV). The imaging scheme, comprising a tunable liquid crystal filter, three-generation GaAsP image intensifier, and CMOS camera, achieves LLL color imaging and ensures sensitivity. The image enhancement and color reconstruction algorithm flow suitable for LLL night vision combines overexposure-against white balance, color correction matrix (CCM) color correction, and color image denoising to improve color visibility and reduce color difference and image noise. The proposed night vision system extends the minimum working illuminance to 10-4 lx and achieves natural and clear color LLL imaging, improving night-time observations.

Low-light-level image super-resolution reconstruction based on iterative projection photon localization algorithm

The intensified charge-coupled device (ICCD) is widely used in the field of low-light-level (LLL) imaging. The LLL images captured by ICCD suffer from low spatial resolution and contrast, and the target details can hardly be recognized. Super-resolution (SR) reconstruction of LLL images captured by ICCDs is a challenging issue. The dispersion in the double-proximity-focused image intensifier is the main factor that leads to a reduction in image resolution and contrast. We divide the integration time into subintervals that are short enough to get photon images, so the overlapping effect and overstacking effect of dispersion can be eliminated. We propose an SR reconstruction algorithm based on iterative projection photon localization. In the iterative process, the photon image is sliced by projection planes, and photons are screened under the constraints of regularity. The accurate position information of the incident photons in the reconstructed SR image is obtained by the weighted centroids calculation. The experimental results show that the spatial resolution and contrast of our SR image are significantly improved.

Effect of pro-proximity pulse voltage on the noise characteristics of generation III low-light-level image intensifiers

To improve the noise performance of low-ligh-level (LLL) image intensifiers, the evaluation of the noise characteristics of a typical generation III image intensifier has been made through the measurement of signal to noise ratio at the output end (SNRout) of this image intensifier in different work conditions. One of the most meaningful work is to discuss the effect of the pro-proximity pulse voltage applied between photocathode and microchannel plate (MCP) on the SNRout of a sample of LLL image intensifier (2008III138). The experimental result shows that the optimal values of voltage across GaAs photocathode and that across MCP are −300V and 800V, respectively. More importantly, the optimal values of high level, low level, and duty circle of the pro-proximity pulse voltage are −300V, 0.2V, and 60%, respectively. This research will provide a theoretical guide and experimental support for developing low noise LLL image intensifiers.

Apparent distance theory revision for low-light-level night vision system based on noise factor

This paper introduces basic concept of the detection range of a low-light-level (LLL) imaging system, and on the basis of previous derivation of visual distance detection equation for the LLL imaging system, the influence of noise factor on visual distance of LLL night vision system is discussed emphatically. Image intensifier is the most important part of LLL night vision device and SNR is one of the important parameters of micro-light image intensifier, whose value determines the detection distance and image sharpness of LLL imaging system in low-light conditions. In this paper, using system noise factor relation, by modifying the noise factor, a more practical and perfect horizon detecting equation is established, and the horizon estimation is carried out with the experiment, and the practicability of the corrected Horizon formula is verified. It has a certain guiding significance for improvement of the LLL imaging system and the development of night vision technology.

Study on magnetic mirror array image intensifier to work at room temperature.

In order to improve the detection capability of the current low-light-level (LLL) imaging systems at room temperature, a new device, a magnetic mirror array image intensifier (MMAII), is proposed in this paper. A magnetic mirror array device (MMAD) is coupled into an image intensifier which sits between the photocathode and the microchannel plate (MCP). The trace photoelectrons, one after another, are first sufficiently accumulated by the MMAD over a long time at room temperature, and then they are released and enter the MCP for further gain. These two steps are used to improve the detection capability at the LLL imaging system at room temperature. After the two-dimensional magnetic field distribution of the magnetic mirror array (MMA) is calculated, the MMA is designed and optimized with a rubidium Nd-Fe-B permanent magnet. Three groups of ideal parameters for the Nd-Fe-B permanent magnet MMAD, with a magnetic mirror ratio of 1.69, for all of them have been obtained. According to the research results on the noise of the escape cone of the MMAII, the angle between the incident direction and the axis is greater than 57°, so the trace electrons must be constrained by the magnetic mirror. We made 54 MMAs from Nd-Fe-B permanent magnets and packaged them in a container. Then the system was evacuated to 10^-3 Pa at room temperature. It was found by experiment that the trace electrons could be actually constrained by the MMAD. The MMAII can be applied to images for static LLL objects.

Local Sparse Structure Denoising for Low-Light-Level Image.

Sparse and redundant representations perform well in image denoising. However, sparsity-based methods fail to denoise low-light-level (LLL) images because of heavy and complex noise. They consider sparsity on image patches independently and tend to lose the texture structures. To suppress noises and maintain textures simultaneously, it is necessary to embed noise invariant features into the sparse decomposition process. We, therefore, used a local structure preserving sparse coding (LSPSc) formulation to explore the local sparse structures (both the sparsity and local structure) in image. It was found that, with the introduction of spatial local structure constraint into the general sparse coding algorithm, LSPSc could improve the robustness of sparse representation for patches in serious noise. We further used a kernel LSPSc (K-LSPSc) formulation, which extends LSPSc into the kernel space to weaken the influence of linear structure constraint in nonlinear data. Based on the robust LSPSc and K-LSPSc algorithms, we constructed a local sparse structure denoising (LSSD) model for LLL images, which was demonstrated to give high performance in the natural LLL images denoising, indicating that both the LSPSc- and K-LSPSc-based LSSD models have the stable property of noise inhibition and texture details preservation.

A fusion algorithm for infrared and low light level images based on edge information and support value transform

In this paper, a fusion algorithm for infrared and LLL images based on edge information and support value transform is proposed. Firstly, the infrared and LLL images are combined after edge detection to obtain the first fused image. Secondly, the infrared, LLL and the first fused images are decomposed respectively by using the support value transform (SVT) to obtain the sequences of their approximations and support values. Thirdly, the multiple sets of sequences of approximations and support values are combined together. Finally, the fused images are recovered by using the inverse support value transform (ISVT). The experiments and results demonstrate that the fused image which uses the algorithm presented in this paper has the advantage of better visual effect. It has preserved the detailed information effectively and has extracted more edge information from source images compared with the conventional fusion algorithms.

Phosphor screen's FM for LLL image intensifier

The luminescent properties of the phosphor screen of LLL image intensifier has a very close correlation with the performance of the LLL image intensifier. As for the research status of luminescent properties of phosphor screen of the LLL image intensifier, the test, analysis and evaluation research for the various performance parameters of its light-emitting properties, such as luminous efficiency, resolution, are relatively independent, which is not conducive to the overall performance evaluation of a phosphor screen. In view of this, on the basis of the analysis of the correlation between LLL image intensifier performance and the phosphor screen performance, and according to the concept of figure of merit (FM) of LLL image intensifier, a new FM concept of the phosphor screen is proposed. By the relevant experiments, the FM expression of a phosphor screen under different technical and excitation conditions was obtained. The performance of the phosphor screen can be evaluated intuitively and comprehensively by means of FM of the phosphor screen. The performance evaluation contributes to the in-depth study on FM of the LLL image intensifier.

Combine Target Extraction and Enhancement Methods to Fuse Infrared and LLL Images

Combine Target Extraction and Enhancement Methods to Fuse Infrared and LLL Images

Role of spectral matching in optimization of the design of low-light-level imaging systems

The combined effect of the spectral variations in the distributions of ambient radiation, the response of the imaging device, and the reflectance of the objects on the field performance of low-light-level (LLL) imaging systems is analyzed. It is shown that the effect can be taken into account by introducing correction factors in terms of luminous equivalence ratio and equivalent contrast. The values of these factors are computed for different combinations of night-sky radiation, photosurfaces, and objects. The effect of these factors on the range of a typical LLL imaging system for certain commonly encountered object–background combinations is discussed with reference to the optimization of the system design.