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Neural Architectures for Correlated Noise Removal in Image Processing

机译：用于图像处理中相关噪声消除的神经架构

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摘要

The paper proposes a new method that combines the decorrelation and shrinkage techniques to neural network-based approaches for noise removal purposes. The images are represented as sequences of equal sized blocks, each block being distorted by a stationary statistical correlated noise. Some significant amount of the induced noise in the blocks is removed in a preprocessing step, using a decorrelation method combined with a standard shrinkage-based technique. The preprocessing step provides for each initial image a sequence of blocks that are further compressed at a certain rate, each component of the resulting sequence being supplied as inputs to a feed-forward neural architecture F-X -> F-H -> F-Y. The local memories of the neurons of the layers F-H. and F-Y are generated through a supervised learning process based on the compressed versions of blocks of the same index value supplied as inputs and the compressed versions of them resulting as the mean of their preprocessed versions. Finally, using the standard decompression technique, the sequence of the decompressed blocks is the cleaned representation of the initial image. The performance of the proposed method is evaluated by a long series of tests, the results being very encouraging as compared to similar developments for noise removal purposes.
机译：本文提出了一种新方法，该方法将去相关和收缩技术与基于神经网络的方法相结合，用于噪声消除。图像被表示为大小相等的块的序列，每个块会因静态统计相关噪声而失真。使用去相关方法和基于标准收缩的技术相结合，可以在预处理步骤中消除块中大量的感应噪声。预处理步骤为每个初始图像提供一系列块序列，这些块序列将以一定速率进一步压缩，将所得序列的每个成分作为输入提供给前馈神经体系结构F-X-> F-H-> F-Y。 F-H层神经元的局部记忆。通过有监督的学习过程，根据作为输入提供的具有相同索引值的块的压缩版本以及它们的压缩版本作为其预处理版本的平均值，通过有监督的学习过程来生成F和Y。最后，使用标准解压缩技术，解压缩块的序列是原始图像的清晰表示。通过一系列测试评估了所提出方法的性能，与出于消除噪音目的的类似开发相比，结果令人鼓舞。

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