A method and device for blind separation of permutation and aliasing images

Abstract

The invention relates to a method and device for blind separation of permuted and aliased images. The method comprises: obtaining a training data set according to at least two permuted and aliased images containing noises with known positions of permuted regions; constructing a convolutional neural network to obtain The training data set is input to the convolutional neural network for training to obtain the trained convolutional neural network; the permutation and aliasing image to be detected is preprocessed and input to the trained convolutional neural network to obtain the image feature map; The image feature map is optimized, and the optimized image feature map is dot-multiplied with the original permutation and aliasing image to be detected to obtain the separation effect map. The invention adopts the convolutional neural network to automatically extract features from the permuted and aliased images, and the extracted features are stable and not affected by human factors, which improves the accuracy of image separation; and solves the separation problem by turning it into a classification problem, simplifying the The separation process is improved and the separation speed is improved.

Description

technical field

[0001] The invention relates to a method and device for blind separation of permutation and aliasing images, belonging to the technical field of signal processing. Background technique

[0002] Blind Source Separation (BSS), also known as Blind Signal Separation (BSS), is based only on the observed mixed output signal without requiring too much source signal and channel prior information. , the process of separating each input source signal is a research hotspot in the field of signal processing, and is widely used in image processing, data transmission, voice signal processing, mobile communication, biomedical signal processing and other fields. Blind separation of replaced and aliased images is to separate the replaced region from the replaced region when the position, size, and number of the replaced region are unknown. After that, domestic scholars carried out further research on this kind of permutation and aliasing images, and obtained some results. ...

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