Image reconstruction system and method based on CRC-SAN network

An image reconstruction and network technology, applied in image enhancement, image analysis, image data processing, etc., can solve the problem of lack of differential learning in the network, hinder the improvement of the performance of convolutional neural networks, etc., and achieve the effect of improving the differential learning of the network.

Active Publication Date: 2021-02-05
CHONGQING UNIV OF POSTS & TELECOMM
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However, in the existing single-image super-resolution reconstruction methods based on convolutional neural networks, most methods ignore the differences in features between different components in the image, but treat the features of different components in the image equally. Th

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  • Image reconstruction system and method based on CRC-SAN network
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  • Image reconstruction system and method based on CRC-SAN network

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[0050] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0051] figure 1 A schematic diagram of the cross residual channel-spatial attention network structure provided for the embodiment of the present invention, in which CRG represents a cross residual group structure (CRG structure such as figure 2 shown), where 3x3Conv represents a 3×3 convolution operation, 1x1Conv represents a 1×1 convolution operation, nxnDeConv represents an n×n deconvolution operation, and nxnConv represents an n×n convolution operation, wh...

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Abstract

The invention relates to the technical field of image super-resolution reconstruction, in particular to an image reconstruction system and method based on a cross residual channel-spatial attention network. The system comprises a shallow feature extraction module, a depth feature extraction module, an up-sampling module and a reconstruction layer. The input of the shallow feature extraction moduleis a low-resolution image, and the shallow feature extraction module is used for extracting shallow features; the depth feature extraction module comprises a frequency division module and a cross residual group, the input of the depth feature extraction module is the output of the shallow feature module, and the depth feature extraction module is used for extracting deep features; the input of the up-sampling module is a deep feature and is used for up-sampling; and the reconstruction layer is used for reconstructing features to obtain a high-resolution image. The reconstruction network provided by the invention has stronger feature expression capability and differentiated learning capability, and can reconstruct a high-quality high-resolution image.

Description

technical field [0001] The invention relates to the technical field of image super-resolution reconstruction, in particular to an image reconstruction system and method based on a cross residual channel-spatial attention CRCSAN network. Background technique [0002] Single-image super-resolution reconstruction means upsampling a low-resolution image to a corresponding scale to obtain a high-resolution image. [0003] Super-resolution reconstruction of a single image is a serious pathological problem, because there may be multiple different high-resolution images corresponding to a single low-resolution image during the process of resolution increase, in other words, there will be There are multiple solutions. Therefore, in order to solve the above problems, a large number of single image super-resolution reconstruction methods based on convolutional neural networks have been proposed. However, in the existing single-image super-resolution reconstruction methods based on co...

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Application Information

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IPC IPC(8): G06T3/40G06N3/04G06N3/08
CPCG06T3/4053G06T3/4038G06N3/084G06T2207/10004G06T2207/20081G06T2207/20084G06T2200/32G06N3/045
Inventor 唐述杨书丽
Owner CHONGQING UNIV OF POSTS & TELECOMM
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