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Grayscale image contrast enhancement based on multi-scale edge representation

机译:基于多尺度边缘表示的灰度图像对比度增强

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We propose an image contrast enhancement algorithm using multi-scale edge representation of images. It has long been known that the Human Vision System (HVS) heavily depends on edges in the understanding and perception of scenes. Contrasts in grayscale images are measured between the differences of pixels on both sides of edges, which is defined as the gradient magnitudes of those edges. And multi-scale edge of an image is characterized by the local extrema of wavelet coefficients across levels. So rebuilding an image from properly stretched the extrema is a promising way to enhance the contrast of the image. We tackle this reconstruction problem with a straightforward interpolation method instead of the commonly used iterative projection process. Extensive experiments justify our algorithm an efficient and effective contrast enhancement method.
机译:我们使用图像的多尺度边缘表示提出了一种图像对比度增强算法。众所周知,人类视觉系统(HVS)大量取决于理解和对场景的感知的边缘。在边缘两侧的像素的差异之间测量灰度图像中的对比度,该边缘的差异被定义为这些边缘的梯度幅度。图像的多尺度边缘的特征在于跨越小波系数的局部极值。因此,从正确伸展图像重建图像是一个有希望的方式来增强图像的对比度。我们用简单的插值方法代替常用的迭代投影过程来解决这个重建问题。广泛的实验证明了我们的算法有效且有效的对比度增强方法。

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