Image super-resolution reconstruction method based on deep convolution sparse coding

A technology of super-resolution reconstruction and convolutional sparse coding, which is applied in the field of image super-resolution reconstruction based on depthwise convolutional sparse coding, can solve the problems of difficulty in improving network structure, complicated calculation, and time-consuming reconstruction process, and achieves The effect of faster training and convergence, compact network structure, and good interpretability
CN112907449AActive Publication Date: 2021-06-04SOUTHWEST UNIV
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
SOUTHWEST UNIV
Publication Date
2021-06-04

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Abstract

The invention belongs to the technical field of image super-resolution reconstruction, and discloses an image super-resolution reconstruction method based on deep convolution sparse coding, and the method comprises: embedding a multilayer learning iteration soft threshold algorithm ML-LISTA related to a multilayer convolution sparse coding model ML-CSC into a deep convolution neural network DCNN; adaptively updating all parameters in the ML-LISTA by using the learning ability of the DCNN, and constructing an interpretable end-to-end supervision neural network SRMCSC for image super-resolution reconstruction; and introducing residual learning, extracting residual features by using an ML-LISTA algorithm, combining the residual and an input image to reconstruct a high-resolution image, and then accelerating the training speed and the convergence speed. The SRMCSC network provided by the invention is compact in structure, has good interpretability, can provide a result with visual attraction, and provides a practical solution for super-resolution reconstruction.
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Description

technical field

[0001] The invention belongs to the technical field of image super-resolution reconstruction, and in particular relates to an image super-resolution reconstruction method based on deep convolution sparse coding. Background technique

[0002] Currently, image super-resolution reconstruction (SR) is a classic problem in many digital imaging and computer low-level vision, which aims to construct high-resolution images (HR) from single-input low-resolution images (LR), and is widely used It is used in a variety of fields, from security and surveillance imaging to medical imaging and satellite imaging where more image detail is required. This is due to the imperfection of the imaging system, transmission medium and recording equipment, which affects the visual effect of the image. Therefore, in order to obtain high-quality digital images, it is necessary to perform super-resolution reconstruction on the images.

[0003] In recent years, image super-resolution re...

Claims

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