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image enhancement相关的网络例句

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与 image enhancement 相关的网络例句 [注:此内容来源于网络,仅供参考]

Based on genetic algorithm and incomplete beta function, we introduce a self-adaptive method for image contrast enhancement and apply this method to radiograph image enhancement.

基于遗传算法和非完全Beta函数,介绍了一种自适应的图像对比度增强方法,并将其应用到辐射图像的增强处理中。

A method of image enhancement is proposed based on particle swarm optimization,which can automatically find the optimal regularized Beta function to achieve image adaptive enhancement.

文中提出了一种基于微粒群的自适应对比度变换算法,该算法能自动寻找最优的Beta函数参数,实现图像对比度的自适应变换。

Image enhancement refers to the dura image enhancement, the useful information, it can be a distortion of the process, its purpose is to enhance the visual effect.

图像增强是指增强图象中的有用信息,它可以是一个失真的过程,其目的是要增强视觉效果。

Histogram equalization is classical approach of image enhancement. But we usually equipoise the whole image one time, that will bring on insufficiency on the minutia part's enhancement.

直方图均衡是图像增强的经典方法,但通常都是简单地对整幅图像做一次均衡,这样势必对细节部分增强得不够。

Contents : Chapter 4 Image Enhancement (contrast enhancement, gray transform, histogram dressing, image smoothing, sharpening, Pseudo-color and false color enhancement); Chapter 5 of Image Restoration (inverse filter rehabilitation, Wiener filtering method); Chapter 6 of image processing orthogonal transformation Fourier transform, discrete cosine transform (DCT, the Walsh transform, Based on the eigenvectors of transformation, Hotelling transform, SVD transform, wavelet transform, Mallat algorithm; Chapter 7 Image Coding Hoffman (Huffman coding, arithmetic coding, RIc, the bit-plane coding, predictive coding, DPCM.

内容:第4章图像增强(对比度增强、灰度变换法、直方图修整法、图像平滑、图像锐化、伪彩色和假彩色增强);第5章图像复原(逆滤波复原、维纳滤波方法);第6章图像处理中的正交变换(傅立叶变换、离散余弦变换、沃尔什变换、基于特征向量的变换、霍特林变换、SVD变换、小波变换、Mallat算法);第7章图像压缩编码(霍夫曼编码、算术编码、游程编码、位平面编码、预测编码、DPCM、JPEG 2000编码

Image enhancement in image processing is an important part of this process is image enhancement procedures.

详细说明:图像增强是图像处理里重要的一个部分,本程序是图像增强的程序。

In this paper, based on the research on Incomplete Beta Function, we introduce three different methods for implementing self-adaptive image contrast enhancement Traditional image enhancement is to define corresponding gray transform function according to gray distribution of the image.

基于对非完全Beta函数的研究,介绍了三种不同的自适应图像对比度增强方法。

The digital speciemen image of adult Helicoverpa armigera Hubner was acquired using a digital camera in an image acquisition environment.Several methods of image enhancement were covered, including linear smoothing, median filtering, linear greylevel transformation, contrast stretching, image inversing and histogram equalization. Results demonstrated that these methods evidently enhanced the speciemen image of H.

digital image ;本研究以棉铃虫成虫标本作为数字图像源,设计了图像获取环境,以数码相机做为数字图像获取设备获得了棉铃虫成虫的数字图像,介绍了昆虫数字图像增强的方法,包括图像的线性平滑、中值滤波平滑、线性灰度变换、对比度拉伸、图像求反以及直方图均衡化等增强方法,证明这些方法对棉铃虫图像具有明显的增强作用。

How to evaluate the enhancement algorithm is also a difficult problem, from the point of color adaptation, a color image enhancement and evaluation algorithmis given considering the two color characteristics: color naturalness index and color colorfulness index. Finally, we presents a new approach to color image denoising based on Human Visual System model.

随后,本文还从人眼的颜色适应角度出发提出了一能够种评估颜色增强的算法,这种算法对彩色图象的分量按照von-Kries模型重新调整比例,并利用颜色的自然性系数和生动性系数在自然图象意义上给出了评估准则。

This paper describes a new algorithm of image enhancement of impulse noise based on PCNN time matrix and rough sets indiscernibility relation. For noisy gray images, the results of computer simulations show that the image noise can be reduced efficiently and the image becomes clear and the target details of the image can be enhanced better by using this algorithm. In addition, the effects of this algorithm are better than usual image enhancement algorithm.

基于粗集理论中的不可分辨关系的等价概念及PCNN赋时矩阵,提出了一种含有脉冲噪声的图像增强算法,计算机仿真结果表明,对被噪声污染的灰度图像,用基于PCNN与粗集理论相结合的图像增强算法处理后,图像噪声得到有效抑制,画面更清晰,图像中目标的细节也得到了很好的增强,其结果优于常规方法。

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