Image restoration system based on multilevel wavelet convolutional neural network

Abstract

The invention discloses an image restoration system based on a multilevel wavelet convolutional neural network, belongs to the field of image restoration, and solves the problem that an existing imagerestoration system based on a convolutional neural network cannot give consideration to image restoration quality and image restoration speed. According to the system, wavelet transform layers and convolutional neural sub-networks are arranged alternately; the output ends of the wavelet transform layers are connected with the input ends of the convolutional neural sub-networks; the input end of afirst deconvolutional neural sub-network is connected with the output end of the Mth convolutional neural sub-network; the output end of the first wavelet transform layer is further connected with the input end of an Mth invert wavelet transform layer; the output ends of the first to (M-1)th convolutional neural sub-networks are connected with the input ends of the Mth to second deconvolutional neural sub-networks; an input object of the first wavelet transform layer is a to-be-restored image; and an output result of the Mth invert wavelet transform layer is a restored image; and M is greaterthan or equal to 2.

Description

technical field [0001] The invention relates to an image restoration system, which belongs to the field of image restoration. Background technique [0002] In real life, due to the limitation of the image acquisition resolution of the imaging system hardware, the movement of the camera and the change of the light environment during the image acquisition process, the limitation of storage space and transmission bandwidth, the image often needs to be down-sampled and lossy compressed. , resulting in low-quality images such as blurring, noise interference, and low resolution. Such low-quality images not only affect people's visual senses, but also cannot be applied to artificial intelligence applications such as face recognition and object detection that require high-quality input images. Therefore, it is particularly necessary to restore low-quality images to high-quality images. [0003] In recent years, scholars have attempted to apply convolutional neural networks to the ...

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Problems solved by technology

[0004] In order to solve the problem that the existing image restoration system based on convolutional neural network cannot take into account both image resto

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