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Review of applications of partial differential equations for image enhancement

机译:偏微分方程在图像增强中的应用综述

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Image restoration and enhancement are important parts of digital image processing, belonging to the early visual image processing problems. Image pre-processing is the necessary preliminary work of image analysis, such as filtering to reduce image noise and to enhance the image edges. The image enhancement technique plays an important role in improving image quality and is good for image post-processing e.g. image segmentation and image tracking. Image restoration and enhancement have been widely used in military, medical, industrial production and other fields. Partial differential equation (PDE) as a sophisticated method of image analysis and processing is of great values of research and application, which needs a deep study. As both the variational model and the anisotropic diffusion model have a complete theoretical framework, a variety of models and sophisticated numerical schemes, introduction of which to the fields of digital image processing and computer vision provides a powerful tool to solve problems undoubtedly . This paper concerns about the applications of the PDE in image restoration and image enhancement. We mainly assay traditional methods of image analysis, study applications of the variational method and diffusion equations in image restoration, as well as their improved algorithm for image enhancement.  
机译:图像恢复和增强是数字图像处理的重要组成部分,属于早期的视觉图像处理问题。图像预处理是图像分析的必要准备工作,例如进行滤波以减少图像噪声并增强图像边缘。图像增强技术在改善图像质量中起着重要作用,并且对图像后处理(例如图像处理)有好处。图像分割和图像跟踪。图像恢复和增强已被广泛用于军事,医疗,工业生产和其他领域。偏微分方程(PDE)作为一种复杂的图像分析和处理方法具有重要的研究和应用价值,需要深入研究。由于变分模型和各向异性扩散模型都具有完整的理论框架,各种模型和复杂的数值方案,将其引入数字图像处理和计算机视觉领域无疑为解决问题提供了有力的工具。本文关注PDE在图像恢复和图像增强中的应用。我们主要分析传统的图像分析方法,研究变分方法和扩散方程在图像恢复中的应用,以及改进的图像增强算法。  

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