Edge detection is an important part of image segmentation, in this paper, the edge detection algorithm based on traditional Canny operator for medical images is studied. The Canny operator is improved using Otsu algorithm and double-gate limit detection method, and the ability of Canny operator edge detection is strengthened. The simulation of the algorithm is realized on the computer platform by MATLAB, and the experimental results are analyzed from two image objective evaluation indexes of information entropy and mean square error. The experimental results show that compared with the traditional Canny algorithm, the improved adaptive double threshold Canny algorithm has better edge detection effect, richer image details, better noise suppression and less false edges.
A real-time image defogging processing method based on FPGA is proposed to increase low-contrast of the aerial images in foggy vision conditions. According to the high similarity of the contiguous frame histogram in video image, the histogram equalization algorithm is improved to enhance the image contrast. The median filter algorithm is used to eliminate the noise of the image. The experimental results demonstrate that the method can efficiently enhance the contrast, strengthen the detail and clarify definition for the fog-degraded aerial images. The visibility limit of the images has been increased to a time above. Simultaneously the method satisfies the real-time processing requirement of aerial video images, which has direct applications to the problem of poor visibility conditions.
The images of outdoor scenes obtained in haze, fog and other weather days are usually have poor contrast and color fidelity. In this paper, in order to effectively improve the degraded image in haze quality, reduce the effect of the haze to outdoor traffic video monitoring systems, we analyzed the image degradation reason and fuzzy mechanism of image in haze. From the viewpoints of image restoration and image enhancement, an efficient and real-time image haze removal approach in view of the global dark-channel prior theory and image contract extending was proposed. Firstly, we used the global dark-channel prior method to remove the haze and fog, and then adopted the histogram equalization to enhance the contract and the brightness of images. The experimental results showed that the approach directly recovered a clear and quality haze-free image, obtained satisfactory visual effect. For the practical engineering applications, a high performance image acquisition, enhancement, haze removal and transmission platform was designed. It used the FPGA (field programmable gate array) as the core processer, and the algorithm proposed in this paper was implemented on the platform. The simulation result of timing sequenceandthe actual testing resultwereverified the reliability and validity of the method. Finally, through the actual test results indicated that the system can real-time, effectively enhance the image contrast and color definition of traffic video monitoring systems, thus it can improve its reliability, stability and the ability to cope with the bad weather such as the fog, haze and so on.
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