首页> 外文会议>2014 International Conference on Medical Imaging, m-Health amp; Emerging Communication Systems >Enhancement of contrast and resolution of gray scale and color images by wavelet decomposition and histogram shaping and shifting
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Enhancement of contrast and resolution of gray scale and color images by wavelet decomposition and histogram shaping and shifting

机译:通过小波分解和直方图整形和移位增强灰度和彩色图像的对比度和分辨率

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In this paper a new methodology of enhancement of images is well proposed. This method combines two very popular techniques of enhancement i.e. Wavelet decomposition and histogram shifting & shaping. In this we will use this method for enhancement of commercial images and natural images etc. In this algorithm, a original image (gray scale and color image) is first decomposed in its discrete wavelet coefficients, then these wavelet coefficients filtered by global thresholding. This threshold value is calculated by histogram shifting & shaping method with the variable value of K coefficient. Inverse wavelet transform of filtered and modified wavelet coefficients of image give the reconstruction of original image. With this algorithm, a very new and efficient algorithm for reshaping of histogram that is capable in enhancing local details as well as properly preserving the image contrast, resolution and brightness is presented. In this paper, we show that a modified version of the measurement of enhancement by entropy (EME) can be used as an image similarity measure, and thus an image quality measure and calculated. Until now, EME has generally been used to measure the level of enhancement obtained using a given enhancement algorithm and enhancement parameter. In terms of EME values, this method of combination will gives better results.
机译:在本文中,很好地提出了一种新的图像增强方法。这种方法结合了两种非常流行的增强技术,即小波分解和直方图移位与整形。在这种方法中,我们将使用这种方法来增强商业图像和自然图像等。在这种算法中,首先将原始图像(灰度和彩色图像)分解为其离散的小波系数,然后将这些小波系数通过全局阈值滤波。该阈值通过直方图移位和整形方法使用K系数的可变值来计算。图像的滤波和修正后的小波系数的逆小波变换给出了原始图像的重建。利用该算法,提出了一种非常新颖且高效的直方图整形算法,该算法能够增强局部细节并正确保留图像的对比度,分辨率和亮度。在本文中,我们证明了熵增强测量(EME)的改进版本可以用作图像相似性度量,从而可以计算和计算图像质量度量。到目前为止,一般使用EME来衡量使用给定增强算法和增强参数获得的增强级别。就EME值而言,这种组合方法将提供更好的结果。

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