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Joint coding-denoising optimization of noisy images

机译:噪声图像的联合编码去噪优化

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摘要

In this paper, we propose to study the problem of noisy source coding/denoising. The challenge of this problem is that a global optimization is usually difficult to perform as the global fidelity criterion needs to be optimized in the same time over the sets of both coding and denoising parameters. Most of the bibliography in this domain is based on the fact that, for a specific criterion, the global optimization problem can be simply separated into two independent optimization problems: The noisy image should be first optimally denoised and this denoised image should then be optimally coded. In many applications however, the layout of the acquisition imaging chain is fixed and cannot be changed, that is a denoising step cannot be inserted before coding. For this reason, we are concerned here with the problem of global joint optimization in the case the denoising step is performed, as usual, after coding/decoding. In this configuration, we show how to express the global distortion as a function of the coding and denoising parameters. We present then an algorithm to minimize this distortion and to get the optimal values of these parameters. We show results of this joint optimization algorithm on classical test images and on a high dynamic range image, visually and in a rate-distortion sense.
机译:在本文中,我们建议研究噪声源编码/去噪问题。该问题的挑战在于,通常难以执行全局优化,因为需要同时在编码和降噪参数集上同时优化全局保真度标准。该领域中的大多数参考书目基于以下事实:对于特定准则,可以将全局优化问题简单地分为两个独立的优化问题:噪声图像应首先进行最佳去噪,然后再对经过去噪的图像进行最佳编码。但是,在许多应用中,采集成像链的布局是固定的,无法更改,即在编码之前无法插入降噪步骤。因此,在编码/解码之后照常执行去噪步骤的情况下,我们在这里关注全局联合优化的问题。在这种配置中,我们展示了如何根据编码和降噪参数来表达全局失真。然后,我们提出一种算法,以最小化这种失真并获得这些参数的最佳值。我们以视觉和速率失真的方式在经典测试图像和高动态范围图像上显示了这种联合优化算法的结果。

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