KEYWORDS: Image processing, Image enhancement, Image information entropy, Night vision, Digital image processing, Digital Light Processing, Detection and tracking algorithms, Visualization, Optoelectronics, Infrared technology
When the classical gray stretching algorithm is applied in digital low light level devices, it cannot meet the requirements of illumination environment change in large dynamic range, and the image processed under very low illumination or very high illumination is prone to the loss of target details, an adaptive gray stretching algorithm suitable for digital low light level devices is designed. The algorithm adds the variable of the brightness value of the original image information collected by the digital low light level device to the gray stretching transformation matrix, deduces the current environmental illuminance value according to the transformation of the brightness value, timely adjusts the relevant parameters in the gray stretching calculation matrix according to the environmental illuminance value, and obtains the gray stretching transformation matrix suitable for different image features under different illuminance environments, In order to meet the requirement that digital low light level devices should be suitable for the change of illumination environment in a large dynamic range. This paper also compares the adaptive algorithm with the classical algorithm, and gives the test results. At the end of the paper, the operation efficiency of the algorithm is tested to verify that the algorithm can meet the requirements of real-time image processing speed of digital low light level devices.
Equivalent background illuminance is an important parameter which directly reflects the background illuminance and the background noise of image intensifier assembly. It is analyzed and tested respectively in this paper that the method to measurement equivalent background illuminance based on luminance meter and illuminometer. The results show that the equivalent background illuminance of image intensifier assembly with gallium arsenide photocathode can be measured by the luminance meter or the illuminometer. However, because the background illuminance of image intensifier assembly with aluminum gallium nitride photocathode would be lower than the effective measurement limitation of the luminance meter, it can only be measured with a low light level illuminometer. Error analysis result of using an illuminometer to measure the equivalent background illuminance shows that the measurement error of this method is 9.1%, which can meet the requirements of the equivalent background illuminance measurement of image intensifier assembly. The research of this paper can provide an effective solution for testing the equivalent background illumination of more image intensifiers assembly.
In the process of crack identification for round logs, conventional edge extraction cannot effectively suppress noise because of the tree's annual ring lines and the similarity between the burr noises during cutting and the gray level of the target. Therefore, it is no easy to extract the target crack. The method of continuous gray-scale transformation enhancement is put forward in this thesis to increase the difference between the gray level of the background pixel and the gray level of the target so that can obtain an ideal pre-processed image. In the process of image preprocessing, the method of continuous gray-scale transformation enhancement is applied, that is to combine the gray-scale transformation enhancement and the non-linear filtering process so that can realize the preprocessing of the original image. The gray level difference between the extraction target and the background is increasing under the premise of preserving the image-extraction features. In the extraction process, the extracted target crack image is obtained through utilizing the localization minimum in mathematical morphology and then the compound morphological algorithm is designed based on the basic algorithm of mathematic morphology so as to obtain the target crack image which is connected by the edge curves. Results The MATLAB image processing algorithm is used to simulate each step of the method. The results show that the extracted target crack images are ideal. The mentioned algorit can not only ensure the integrity of the extraction target, but also can suppress the noise very well so that can satisfy the needs during the extraction of complex background images, especially the images with little difference between the background gray level and the extraction target gray level.
Luminance gain is an important parameter to evaluate the light intensity enhancement ability of the low light level image intensifier assembly. The higher the luminance gain, the easier the receiver is to sense and recognize. However, luminance gain is not a directly measurable physical quantity. Thus, luminance gain measuring devices have non-standard specific properties. Based on the principle of luminance gain measuring specified in the standard, the structure and measurement methods of measurement devices are analyzed, the error and optimization methods of two major measurement methods are compared, and the distribution of the combined uncertainty of measuring luminance gain is studied. Then, an optimized measurement scheme of luminance gain of low light level image intensifier assembly is put forward. Based on this scheme, a comprehensive measurement uncertainty analysis is carried out and the calculated luminance gain measurement extended combined uncertainty is about 6.7% (k=2). The results are of great significance for improving the measurement accuracy of luminance gain of low light level image intensifier assembly.
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