Image compressed sensing reconstruction method based on residual dense threshold network

An image compression and dense technology, applied in image coding, image data processing, neural learning methods, etc., can solve problems such as the reconstruction quality needs to be improved, the reconstruction speed is slow, and the interpretability is poor

Pending Publication Date: 2021-06-18
SOUTH CHINA UNIV OF TECH
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  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] In order to overcome the defects and insufficiencies of the existing technology, in order to solve the problems of poor interpretability, slow reconstruction speed and reconstruction quality in the existing reconstruction methods, the present invention provides an image compression sensing re...

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  • Image compressed sensing reconstruction method based on residual dense threshold network
  • Image compressed sensing reconstruction method based on residual dense threshold network
  • Image compressed sensing reconstruction method based on residual dense threshold network

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Embodiment 1

[0050] Such as figure 1 As shown, this embodiment provides a method for image compression sensing reconstruction based on residual dense threshold network, including the following steps:

[0051] S1: The original image set is randomly cropped and divided into a series of original real cropped image blocks (high-dimensional original signal), and for each given cropped image block, it is converted into a one-dimensional vector, and at a selected sampling rate Construct the corresponding measurement matrix sampling through the random Gaussian matrix to obtain the corresponding sampling amount, and obtain a one-to-one corresponding training set;

[0052] In this embodiment, 91 original image sets are randomly cropped and segmented to obtain 88912 cropped image blocks with a size of 33×33. For each given cropped image block {x i |i=1,2,3,...,n}, and convert it into a one-dimensional vector, and construct the corresponding measurement matrix Φ from the generated random Gaussian mat...

Embodiment 2

[0071] This embodiment provides an image compression sensing reconstruction system based on a residual dense threshold network, including: a training set construction module, an initial reconstruction module, an optimized reconstruction module, an iterative training module, and a compressed sensing reconstruction module;

[0072] In this embodiment, the training set construction module is used to divide the original natural image into blocks and then sample through the Gaussian sampling matrix, and make the low-dimensional sampling amount and cropped image blocks into a training set;

[0073] In this embodiment, the initial reconstruction module is used to restore the low-dimensional sampling amount into a high-dimensional initial reconstruction image block through linear convolution;

[0074] In this embodiment, the optimized reconstruction module is used to optimize and reconstruct the initial reconstructed image through the built residual dense threshold network. The residua...

Embodiment 3

[0078] This embodiment provides a storage medium, the storage medium can be a storage medium such as ROM, RAM, magnetic disk, optical disk, etc., and the storage medium stores one or more programs. Residual Dense Thresholding Networks for Image Compressive Sensing Reconstruction.

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Abstract

The invention discloses an image compressed sensing reconstruction method based on a residual dense threshold network, and the method comprises the steps: carrying out the partitioning of an original natural image, carrying out the sampling through a Gaussian sampling matrix, and making a training set through the low-dimensional sampling amount and cut image blocks; recovering the low-dimensional sampling amount into a high-dimensional initial reconstruction image block through linear convolution; further optimizing and reconstructing the initial reconstructed image through the built residual dense threshold network; calculating a corresponding loss function, reducing loss through an optimizer, and carrying out back propagation on corresponding parameters; retaining the corresponding training model when the loss meets the requirement; and outputting the reconstructed image blocks through the training model and splicing the reconstructed image blocks into a final output image. Compared with an existing compressed sensing reconstruction method, the method has obvious advantages in reconstruction quality and reconstruction time.

Description

technical field [0001] The invention relates to the technical field of digital image processing, in particular to an image compression sensing reconstruction method based on a residual dense threshold network. Background technique [0002] Compressed sensing (Compressed Sensing, CS) reconstruction is a typical image technology, in the case of breaking through the theoretical requirements of Nyquist sampling law, by measuring the signal y∈R m×1 Recover the original signal x ∈ R as losslessly as possible n×1 (m<<n). Due to its wide application in the fields of image source coding and wireless broadcasting, compressive sensing reconstruction technology has received a lot of attention and research. The current compressive sensing reconstruction methods can usually be divided into two types: model-driven methods and methods based on Methods of Machine Learning Neural Networks. [0003] Among them, the model-driven method usually fully mines the prior information of the i...

Claims

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Application Information

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IPC IPC(8): G06T9/00G06T3/40G06N3/04G06N3/08
CPCG06T9/002G06T3/4038G06T3/4046G06N3/084G06T2200/32G06N3/048G06N3/045
Inventor 文祥
Owner SOUTH CHINA UNIV OF TECH
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