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首页> 外文期刊>IEEE Transactions on Neural Networks >Weight assignment for adaptive image restoration by neural networks
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Weight assignment for adaptive image restoration by neural networks

机译：通过神经网络进行自适应图像恢复的权重分配

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摘要

This paper presents a scheme for adaptively training the weights, in terms of varying the regularization parameter, in a neural network for the restoration of digital images. The flexibility of neural-network-based image restoration algorithms easily allow the variation of restoration parameters such as blur statistics and regularization value spatially and temporally within the image. This paper focuses on spatial variation of the regularization parameter. We first show that the previously proposed neural-network method based on gradient descent can only find suboptimal solutions, and then introduce a regional processing approach based on local statistics. A method is presented to vary the regularization parameter spatially. This method is applied to a number of images degraded by various levels of noise, and the results are examined. The method is also applied to an image degraded by spatially variant blur. In all cases, the proposed method provides visually satisfactory results in an efficient way.
机译：本文提出了一种方案，该方案通过改变神经网络中的正则化参数来自适应地训练权重，以恢复数字图像。基于神经网络的图像恢复算法的灵活性很容易允许恢复参数的变化，例如模糊统计和图像内在空间和时间上的正则化值。本文关注正则化参数的空间变化。我们首先表明，先前提出的基于梯度下降的神经网络方法只能找到次优解，然后介绍一种基于局部统计的区域处理方法。提出了一种在空间上改变正则化参数的方法。该方法适用于许多因各种噪声而退化的图像，并检查结果。该方法还适用于因空间变化模糊而退化的图像。在所有情况下，所提出的方法均以有效的方式提供了视觉上令人满意的结果。

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