Medical ultrasound images are usually corrupted by the noise during their acquisition known as speckle. Speckle noise removal is a key stage in medical ultrasound image processing. Due to the ill-posed feature of image denoising, many regularization methods have been proved effective. This paper introduces an approach which collaborate both sparse dictionary learning and regularization method to remove the speckle noise. The method trains a redundant dictionary by an efficient dictionary learning algorithm, and then uses it in an image prior regularization model to obtain the recovered image. Experimental results demonstrate that the proposed model has enhanced performance both in despeckling and texture-preserving of medical ultrasound images compared to some popular methods.
In this paper, the influences for one LWIR thermal camera in different integrate time are studied by using surrounding temperature simulator and blackbody of independent research and development, which compared to theoretical results. In experiment, the image of camera is obtained by using different integrate time and same temperature of blackbody at surrounding temperature of-30°C, 0°C and 20°C, the result show that the output voltage is linearly proportion to integrate time in those surrounding temperature. The experiment is studied for different blackbody temperature at surrounding temperature of-30°C, the result shows that: the slope of “integrate time vs. DL value” is increased with the target temperature. The noise of output image is increased with the increasing of integrate time. Whose agree with theoretical result. According to the conclusion, the DL value of random integrate time can be derived by known two integrate time DL for one blackbody temperature. And the calibration in random integrate time between two integrate time whose calibration curve are known, which is verified in experiment: the blackbody brightness deviation between test and derivation is less than 1%, which corresponds to blackbody temperature deviation between test and derivation is less than 1°C. The experiment results show that the measurement efficiency can be improved by using appropriate integrate time.
Proc. SPIE. 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018)
KEYWORDS: Target detection, Electronics, Lithium, Detection and tracking algorithms, Image processing, Chromium, Pollution, Information technology, Binary data, RGB color model
Image matching is an important topic in the field of computer vision, in view of high robustness and accuracy, SIFT or the improved methods based on SIFT is generally used for image matching algorithms. The traditional SIFT method is implemented on grayscale images without regard to the color information of images, which may cause decreasing of the matching points and reduction of the matching accuracy. Prevailing color descriptors can effectively add color information into SIFT, however dramatically increase the complexity of algorithm. In this paper, a novel approach is proposed to take advantage of the color information for image matching based on SIFT. The proposed algorithm uses the gradient information of color channel as the compensation of luminance channel, which can effectively enhance the color information with SIFT. Experimental results show that the number of feature points and matching accuracy can be significantly promoted, while the complexity and performance of image matching algorithm are well trade-off.
An array extended blackbody is designed to quantitatively measure and evaluate the performance of infrared imaging systems. The theory, structure, control software and application of blackbody are introduced. The parameters of infrared imaging systems such as the maximum detectable range, detection sensitivity, spatial resolution and temperature resolution can be measured.
Calibration is important to the application of infrared camera. For improving low temperature infrared
measurement accuracy. A small extend blackbody is specially designed to meeting the requirement of
low temperature calibration. The temperature range of blackbody is 5℃~90℃ with effective radiation
area 150mm×150mm.The design adopts thermoelectric cooler as driver and chooses the PID
temperature control algorithm. By testing the stability, emissivity and uniformity of blackbody, the
design is proved quiet practical, which meets the needs of infrared camera, spectrometer and other
measuring equipment in low temperature calibration.
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