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Filtering combined dynamic stochastic resonance for enhancement of dark and low-contrast images

机译：过滤组合的动态随机共振以增强暗和低对比度图像

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摘要

Dynamic stochastic resonance (DSR) based dark and low-contrast image enhancement has attracted more and more attention in recent years. For DSR based image enhancement, noise is essential and will be enhanced simultaneously with the contrast of the image, which is undesirable for improvement of perceptual quality. Nonlinear anisotropic diffusion (NAD) is one of the most widely used denoising methods due to good performance of edge preservation, but often fails for contaminated images with high level of noise. In this paper, we propose a novel partial differential equation method for image enhancement by introducing filtering into the stochastic resonance equation, and we consider two kinds of NAD filters. Numerical results demonstrate that the improved methods can not only increase brightness and contrast of the dark and low-contrast images efficiently by optimum iterations, but also remove the noise while preserving edges well, and therefore can achieve good perceptual quality.
机译：近年来，基于动态随机共振（DSR）的深色和低对比度图像增强吸引了越来越多的关注。对于基于DSR的图像增强，噪声是必不可少的，噪声将与图像的对比度同时增强，这对于改善感知质量是不希望的。非线性各向异性扩散（NAD）由于具有良好的边缘保留性能，因此是最广泛使用的降噪方法之一，但对于高噪声水平的受污染图像，通常无法奏效。在本文中，我们通过将滤波引入到随机共振方程中，提出了一种新的偏微分方程图像增强方法，并考虑了两种NAD滤波器。数值结果表明，改进的方法不仅可以通过优化迭代有效地提高暗和低对比度图像的亮度和对比度，而且可以在保留边缘的同时消除噪声，从而获得良好的感知质量。

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