Extreme-Low-Light CMOS has been widely applied in the field of night-vision as a new type of solid image sensor. But if the illumination in the scene has drastic changes or the illumination is too strong, Extreme-Low-Light CMOS can’t both clearly present the high-light scene and low-light region. According to the partial saturation problem in the field of night-vision, a HDR image fusion algorithm based on the Laplace Pyramid was researched. The overall gray value and the contrast of the low light image is very low. We choose the fusion strategy based on regional average gradient for the top layer of the long exposure image and short exposure image, which has rich brightness and textural features. The remained layers which represent the edge feature information of the target are based on the fusion strategy based on regional energy. In the process of source image reconstruction with Laplacian pyramid image, we compare the fusion results with four kinds of basal images. The algorithm is tested using Matlab and compared with the different fusion strategies. We use information entropy, average gradient and standard deviation these three objective evaluation parameters for the further analysis of the fusion result. Different low illumination environment experiments show that the algorithm in this paper can rapidly get wide dynamic range while keeping high entropy. Through the verification of this algorithm features, there is a further application prospect of the optimized algorithm. Keywords: high dynamic range imaging, image fusion, multi-exposure image, weight coefficient, information fusion, Laplacian pyramid transform.
It is prevalent for the low-light night-vision helmet to equip the binocular viewer with image intensifiers. Such equipment can not only acquire night vision ability, but also obtain the sense of stereo vision to achieve better perception and understanding of the visual field. However, since the image intensifier is for direct-observation, it is difficult to apply the modern image processing technology. As a result, developing digital video technology in night vision is of great significance. In this paper, we design a low-light night-vision helmet with digital imaging device. It consists of three parts: a set of two low-illumination CMOS cameras, a binocular OLED micro display and an image processing PCB. Stereopsis is achieved through the binocular OLED micro display. We choose Speed-Up Robust Feature (SURF) algorithm for image registration. Based on the image matching information and the cameras’ calibration parameters, disparity can be calculated in real-time. We then elaborately derive the constraints of binocular stereo display. The sense of stereo vision can be obtained by dynamically adjusting the content of the binocular OLED micro display. There is sufficient space for function extensions in our system. The performance of this low-light night-vision helmet can be further enhanced in combination with The HDR technology and image fusion technology, etc.
The effective distance of the optical imaging system based on CCD/CMOS is affected strongly by fog or haze on the
border less travelled by or the sea level, so this paper aims to adopt an effective method to use near-infrared filter and
digital image processing to increase the system effective distance. Firstly, this paper analyzes theoretically that the
system has a longer visual distance in the near-infrared than that in the visible light in the low visibility condition, and
makes clear that the visual distance of the system will increase to about 1.5 times as much as before. Secondly, given the
border/ coastal surveillance having the characteristics of broad visual angle and the large distance between the observed
targets, this paper works out a partially overlapped sub-block local histogram equalization algorithm, which will achieve
the real-time image enhancement processing of beyond visual range optical imaging on the condition of enhancing the
contrast and maintaining the image specifics. Thirdly, it has developed a real-time enhancement image processing
system of beyond visual range photoelectric image with high-performance DSP and FPGA. And the observed distance of
the system can realize more than two times as much as the visibility in the weather condition with the visibility is about 7 KM.
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